Scientific research

Improving energy efficiency and increasing the level of intelligence are two main factors determining the current development trends for new and modernized buildings. They are especially important in the perspective of development of prosumer installations and local microgrids. A key tool to achieve these goals is a well-designed and implemented Building Automation and Control System (BACS). This paper presents a new hybrid approach to the design and technical organization of BACS based on the provisions of the EN 15232 standard and the guidelines of the Smart Readiness Indicator (SRI) defined in the Energy Performance of Buildings Directive 2018 (EPBD 2018). The main assumptions of this hybrid approach along with examples of functional BACS designs for small prosumer installations organized according to them are provided. Potential impact on building energy performance is discussed as well. Finally, a SWOT analysis of the possibility of merging the EN 15232 standard guidelines and the SRI assessment methodology to develop uniform technical guidelines for the BACS functions design and evaluation of their impact on the buildings’ energy efficiency are discussed.

The rapid development of artificial intelligence (AI) and machine learning (ML) has made it topical to consider learning ability as one of the key performance characteristics of buildings. So far, the buildings’ learning ability has not explained or clarified by definitions or in terms of the proposed frameworks of key performance indicators (KPI). In this paper, a novel performance indicator based on the concept of learning gain is developed to quantify the learning ability of buildings by way of a single, dimensionless number between zero and unity. The implementation of the new Learning Ability Index (LAI) is demonstrated by way of three different case studies chosen from the literature. It is concluded that LAI is an easy and illustrative tool to assess the learning ability of buildings. Particularly, it is useful for monitoring the performance of data-driven processes, when pursuing the preferred strategies to reach higher levels of building intelligence. The LAI considers the time invested in learning plus the quality and diversity of learning material. It is flexible with respect to system boundaries or the performance metrics, wherefore it can be implemented as a generic indicator of system evolution, as well.

Compliance checking is a very important step in engineering construction. With the development of information technology, automated compliance checking (ACC) has been paid more and more attention by researchers. One of the most important steps in automated compliance checking is the representation of the code information. However, the relationship constraint is often ignored in the code information and spatial geometric relationship is challenging to represent. The general code representation method does not have enough ability to identify the situation that does not meet the checking conditions because it is easy to cause semantic ambiguity in the checking results. This paper proposes a code representation method, and the building code information is represented in five parts. Relationships in the engineering domain and spatial relationships can be represented in constraint mode; different spatial relationship constraint-checking methods are also explicated. Constraint subject and constraint item can distinguish checking conditions and requirements, which supports semantic checking results. The mapping between the building information ontology and the code concepts is established, which can be used to automatically generate reasoning rules for compliance checking. Finally, the proposed method is verified by the representation of the China Metro Design Code and the application of the actual Metro model.

Today's construction industry relies heavily on high-performing building information modelling (BIM) systems. By deploying the Industry Foundation Classes (IFC) as a description language, these systems offer building information in a widely interoperable format, so that several applications are able to infer extra information. For a certain functionality, IFC shows limitations however. Existing semantic web technology may be able to overcome these limitations, thereby enabling a range of significant improvements and possibilities for automation in building design and construction. This paper gives a short overview of the functionality of IFC as a language, compared to the functionality of languages deployed in the semantic web domain. The improvements generated by deploying semantic web languages are briefly discussed, after which a concrete implementation approach is presented for a semantic rule checking environment for building design and construction. An implemented test case for acoustic performance checking illustrates the improvements of such an environment compared to traditionally deployed approaches in rule checking.

Porpose The purpose of this paper is to standardised four-step flexibility assessment methodology for evaluating the available electrical load reduction or increase a building can provide in response to a signal from an aggregator or grid operator. Design/methodology/approach The four steps in the methodology consist of Step 1: systems, loads, storage and generation identification; Step 2: flexibility characterisation; Step 3: scenario modelling; and Step 4: key performance indicator (KPI) label. Findings A detailed case study for one building, validated through on-site experiments, verified the feasibility and accuracy of the approach. Research limitations/implications The results were benchmarked against available demonstration studies but could benefit from the future development of standardised benchmarks. Practical implications The ease of implementation enables building operators to quickly and cost effectively evaluate the flexibility of their building. By clearly defining the flexibility range, the KPI label enables contract negotiation between stakeholders for demand side services. It may also be applicable as a smart readiness indicator. Social implications The novel KPI label has the capability to operationalise the concept of building flexibility to a wider spectrum of society, enabling smart grid demand response roll-out to residential and small commercial customers. Originality/value This paper fulfils an identified need for an early stage flexibility assessment which explicitly includes source selection that can be implemented in an offline manner without the need for extensive real-time data acquisition, ICT platforms or additional metre and sensor installations.

The increasing integration of renewable energy sources into the electricity sector for decarbonization purposes necessitates effective energy storage facilities, which can separate energy supply and demand. Battery Energy Storage Systems (BESS) provide a practical solution to enhance the security, flexibility, and reliability of electricity supply, and thus, will be key players in future energy markets. Directive 2019/944, which focuses on common rules for the internal market of electricity, provides a regulatory framework for the deployment of energy storage facilities. However, several gaps and challenges remain regarding the implementation of the directive, particularly in insular energy systems with immature storage infrastructures such as Cyprus, an EU Member State. This study examines these challenges and gaps by investigating the case study of Cyprus while also presenting the handling of energy storage in other European countries such as Germany and Poland. The primary aim of this study is to identify gaps in the legislation regarding energy storage and potential bottlenecks or monopolistic approaches that could hinder the widespread deployment of BESS under the liberalization of the energy market. In light of several BESS technologies available in the market, the study focuses on lithium-based technologies, which account for the largest share of the BESS market and are projected to grow at the highest compound annual growth rate by 2030. Therefore, the authors concentrate on Lithium BESS. The study highlights the crucial role of storage facilities in transforming the power generation sector by shifting toward renewable sources of energy. As such, the study emphasizes the importance of effective regulatory frameworks in enabling the deployment of BESS, particularly in insular energy systems. Overall, this study sheds light on the gaps and challenges facing the deployment of BESS, providing valuable insights for policymakers and stakeholders to design effective regulatory frameworks to facilitate the widespread adoption of BESS.

Although more assessment and certification schemes aimed at buildings appear on the market, professionals always face the same challenges: information scarcity and data flow interruptions. It therefore becomes crucial to rigorously assess the information workflows associated with built assets in order to help deliver the subsequent assessment services and certification schemes. The Smart Readiness Indicator is a new assessment scheme directed at harmonizing the smartness levels of buildings and intelligent installations at a European level. While the European Union defines the Smart Readiness Indicator scope and assessment methodology towards new regulations with the member states, the availability of data should strategically rely on existing sources such as the Building Information Model in order to automate and simplify the efforts of assessors. This paper explores the potential of Building Information Model data, more specifically relying on the Industry Foundation Classes schema, to support assessors with more automatic extraction of relevant information on the building and its equipment. The adopted methodology looks at the semantic alignment between the two domains. An initial alignment of concepts from several versions of the Industry Foundation Classes is proposed. This alignment was implemented using several rules, which were tested on the architectural and mechanical models of the same building. The study shows the convenience of employing such a methodology, the usefulness of data from existing building models, but also their limitations in correctly identifying relevant concepts.

Intelligent building management systems are proven to lead to energy savings and are an integral component of smart buildings. The procedures developed in the EN standards describe the methodology for calculating the energy savings achieved by improving the automation and control levels of separate services in building systems. However, although this method is used in practice, it is rarely applied or investigated by the research community. Typically, energy savings resulting from a single automation improvement intervention in a building heating system are observed, while the holistic view of combined automation upgrades is not considered. Therefore, the purpose of this study was to assess the energy savings resulting from several upgrades to control levels in the heating system components of the building. In addition, this research provides a rationale for the impact of multiple automation and control options for heating systems as well as examines the difference in energy savings. Finally, an analytical model is developed and demonstrated to assess the feasibility of building automation and control upgrades, by determining the allowed investment according to a set of predefined indicators.

The improvement of the energy performance of buildings is identified as one of the core challenges toward achieving a carbon-neutral built environment. In the 2018 recast of the Energy Performance of Buildings Directive, the European Commission has emphasized the need for improved schemes, to ensure the best possible evaluation of the actual energy performance of buildings, taking into consideration all the parameters related both to their construction and operation. Significant research efforts have been designated in this area, to identifying the additional information required to not only improve the energy performance certification process but also to provide more thorough reports to end-users. To increase comprehension, awareness, and thus genuine involvement, cutting-edge digital technologies are expected to be used. The research project entitled “Next-generation Dynamic Digital EPCs for Enhanced Quality and User Awareness” (D^2EPC GA 892984) introduces a comprehensive approach to next-generation Energy Performance Certificates that addresses the main challenges and gaps in buildings' energy assessment process, by introducing additional layers of information for the assessor and the user. A non-exhaustive list of the novel set of Energy Performance Certificates indicators, proposed in D^2EPC includes energy, smart readiness, wellbeing, comfort, financial, and sustainability related indicators. In this study, aspects of employing advanced digital solutions, like Building Information Modelling and Geographical Information Systems for the certification process are also demonstrated, through a well structured, high-level, detailed representation of the next-generation Energy Performance Certificates system architecture. This framework elaborates on individual components and their interaction, toward delivering the envisioned final enriched cloud-based platform, that will enable dynamic Energy Performance Certificates based on (near) real-time field data. This study aspires to initiate the discussion within the scientific community of buildings' energy assessment on the required practices for digitizing and enriching the certification process of buildings, in compliance with Industry 4.0 practices.

The European Commission (EC) recently introduced the smart readiness indicator (SRI) scheme. This framework evaluates the capacity of a building to use information and communication technologies (ICTs) to adapt to the needs of the occupants and the grid. Researchers and industry practitioners have carried out preliminary studies to examine its methodology and scope. This study aimed to complement previous work and analyse the new framework to identify its strengths, as an indicator that will enhance the performance of the building stock, and its improvement opportunities. For this purpose, an evaluation was conducted, following a review of the current literature and the implementation of the proposed methods in two non-domestic buildings. The evaluation indicated that the SRI has the potential to offer multiple benefits, from improving occupants’ health and wellbeing to increasing the energy efficiency of the European building stock and supporting the development of smart energy grids. However, SRI is an indicator that in its current format, cannot address actual performance, and its assessment may not provide equal incentive for all the European Union’s (EU) goals for 2050. Moreover, its simplified methodology was found to offer more favourable results when compared against the detailed methodology. Its checklist approach and lack of clear guidelines may also lead to subjective decisions during the assessment, resulting in inconsistent certifications. By addressing these inconsistencies and by supporting the scheme with additional policy measures, the EU will benefit from a credible and fairer rating scheme. Keywords: Smart readiness indicator, SRI, framework evaluation, smart buildings, European policy.

The efficiency, flexibility, and resilience of building-integrated energy systems are challenged by unpredicted changes in operational environments due to climate change and its consequences. On the other hand, the rapid evolution of artificial intelligence (AI) and machine learning (ML) has equipped buildings with an ability to learn. A lot of research has been dedicated to specific machine learning applications for specific phases of a building's life-cycle. The reviews commonly take a specific, technological perspective without a vision for the integration of smart technologies at the level of the whole system. Especially, there is a lack of discussion on the roles of autonomous AI agents and training environments for boosting the learning process in complex and abruptly changing operational environments. This review article discusses the learning ability of buildings with a system-level perspective and presents an overview of autonomous machine learning applications that make independent decisions for building energy management. We conclude that the buildings’ adaptability to unpredicted changes can be enhanced at the system level through AI-initiated learning processes and by using digital twins as training environments. The greatest potential for energy efficiency improvement is achieved by integrating adaptability solutions at the timescales of HVAC control and electricity market participation.

The last release of the Energy Performance of Buildings Directive 2018/844/EU stated that smart buildings will play a crucial role in the future energy systems. Consequently, the Directive introduced the Smart Readiness Indicator in order to provide a common framework to highlight the value of building smartness across Europe. The methodology for the calculation of the Smart Readiness Indicator is currently under development and therefore not yet officially adopted at the European Union level. In this context, the current research analyzed the second public release of the proposed methodology, discussing the feasibility of its implementation and the obtained results through a practical application. Specifically, the methodology was applied to a nearly zero-energy office building located in Italy, and the evaluation was carried out in parallel by two different expert groups composed by researchers and technical building systems specialists. With the aim of analyzing the impact of subjective evaluations on the calculated indicator, a two-step assessment was adopted: in a first phase the two groups worked separately, and only in a second phase they were allowed to compare results, discuss discrepancies and identify the difficulties in applying the methodology. As the main outcome of this research, a set of recommendations are presented for an effective broad implementation of the Smart Readiness Indicator, able to increase the relevance of its evaluation and effectiveness, as well as to enhance the comparability of smart readiness of buildings through the definition of benchmarks and to integrate with other measurable key indicators, especially concerning energy flexibility.

In the EU’s revised Energy Performance of Buildings Directive (EPBD), a smart readiness indicator (SRI) was introduced as an energy efficiency activity to promote smart ready technologies (SRT) in the building sector. The proposed methodology is based on the evaluation of building services and how they contribute on SRT. The purpose of this paper is to explore the applicability of the SRI to cold climate countries in Northern Europe. The Northern European countries are an interesting test environment for the indicator because of their advanced information and communication technology and high building energy consumption profiles. The findings imply that regardless of the SRI’s conceptualization as a system oriented (smart grid) approach, in its current form, it was not able to recognize the specific features of cold climate buildings, specifically those employing advanced district heating (DH) systems. Another, more practical, implication of the study was that due to the subjective nature of the proposed process for selecting SRI relevant building services, the applicability of SRI as a fair rating system across the EU member states is problematic.

Recent years have seen an increasing need to invest in smart, energy-efficient technologies in buildings to improve health and convenience for occupants and to reduce energy consumption and carbon emission impacts. Digitalization and developments in the information and communication technology sector play a critical role in improving the efficiency of the European energy market and remaining in the current progress of sustainable and renewable energy systems. Therefore, the European Union Member States are required to establish an optional common scheme for defining and calculating a descriptor, called a smart readiness indicator to assess the capabilities of buildings to adapt their operation to the needs of the occupants and the electricity grid and to achieve more efficient operation. This indicator is calculated using a methodology proposed and developed by the European Commission Directorate-General for Energy which depends to a great extent on various factors such as building type and climate conditions. The effectiveness of these parameters on the Smart Readiness Indicator is reflected by the weighting coefficients which need to be initially defined by the legislator. Therefore, the main step to assess the viability of this methodology, is to test it in different situations, i.e., various building types, climate conditions, etc. To this end, the proposed methodology is applied in two service buildings with different levels of energy and indoor environment quality performance located in an area with a Mediterranean climate. The possible effects of smart services and retrofit actions on indoor environment quality and energy performance in the buildings were assessed through energy simulation for two separate rooms in the buildings, a monitoring campaign and a survey to assess the occupants’ subjective opinion about the indoor environmental quality using the questionnaire proposed by the Centre for the Built Environment of the University of California. The results imply that, although the proposed methodology was able to recognise the overall characteristics of the sample buildings, some amendments are still required to capture the specific features of non-residential Mediterranean climate buildings. More specifically, the defined weighting factors fail to reflect the actual energy performance of the service buildings and need to be revised. Moreover, the current retrofitting actions which were implemented to improve energy efficiency and thermal comfort in the building were not as effective as expected in enhancing the SRI value.

Digital innovation is leading to a greater uptake of smart technologies in the building industry. Smart systems can reduce the built environment carbon footprint, improve occupant productivity and enable buildings to participate in managing the electricity network. However, their deployment poses significant challenges, given the heterogeneity of hardware and software in the building environment. Different research approaches have attempted to quantify the preparedness of buildings to facilitate new technologies that improve their performance, while maintaining occupant comfort and allowing optimal delivery of each building's particular service. One prominent approach is the European Commission's Smart Readiness Indicator (SRI). In this paper, we explore how well the SRI applies to Australian buildings by considering a case study building. Our analysis shows that building services need to be carefully chosen to ensure that they are relevant. We therefore advocate adjustment of weighting factors that consider climatic conditions and suitable domains such as heating or cooling. On the basis of our findings, we provide recommendations for adopting the SRI in Australian buildings.

Buildings contribute with 32% to the world's overall energy consumption. Given documented discrepancies between the design and operation of buildings, concepts such as continuous commissioning and performance testing (PTing) have emerged. To increase the applicability and customizability of real-time performance monitoring, PTing frameworks utilize metadata schemas and generic libraries of tests. However, real-time PTing requires enhanced instrumentation in the building, such as meters and sensors. Considering the usage and age of buildings, different buildings impose more stringent requirements for sensing equipment in terms of applicability of PTing. Prior work establishes a framework for automatic discovery of performance tests, and their continuous execution without human intervention. In this paper, we provide an algorithm for automatic estimation of the Smart Readiness Indicator (SRI), a metric for measuring the technological readiness of a building proposed by the European Commission. In line with our PTing framework, we estimate a minimal SRI for PTing to be 23%, implying high applicability across a variety of buildings. We evaluate a case study building for its SRI and perform sensitivity analyses of the SRI specific requirements against the PT-SRI.

Building Information Modeling (BIM) is a process that collects building data in a central data model. This data can not only be used to plan and construct a building but to design the controls for heating, ventilation and air conditioning (HVAC) systems. The relevant information about the building and its systems is used as the base for controller design, opening new opportunities like the automated testing and optimization of control strategies, both for energy efficiency and user comfort. This paper shows how the information in the BIM is used to design control strategies, including a completeness check and a resulting data set enhancement of this necessary information. It also shows a way, how a building, which is already operating, can be optimized using the operation data from energy systems to modify the existing controllers. The methodology is executed using a ventilation system that provides air quality by means of CO2-driven control.

This paper surveys rule checking systems that assess building designs according to various criteria. We examine five major industrial efforts in detail, all relying on IFC building models as input. The functional capabilities of a rule checking system are organized into four stages; these functional criteria provide a framework for comparisons of the five systems. The review assesses both the technology and structure of building design rule checking, as an assessment of this new emerging field. The development of rule checking systems for building is very young and only limited user experience is presented. The survey lays out a framework for considering research needed for this area to mature.

Thermally activated building systems (TABS) integrate the building structure as energy storage, and have proofed to be energy efficient and economic viable for the heating and cooling of buildings. Although TABS are increasingly used, in many cases control has remained an issue to be improved. In this paper, a method is outlined allowing for automated control of TABS in intermittent operation with pulse width modulation (PWM). This method represents one part of a TABS control solution with automatic switching between cooling and heating modes for variable comfort criteria which was published before. A first pulse width modulation control solution is derived based on a simple 1st order model of TABS. Then a second, even simpler solution is given that significantly reduces the tuning effort. Finally, the paper outlines a pulse width modulation control procedure and gives two application examples of the PWM control carried out in a laboratory test room.

Energy flexibility, through short-term demand-side management (DSM) and energy storage technologies, is now seen as a major key to balancing the fluctuating supply in different energy grids with the energy demand of buildings. This is especially important when considering the intermittent nature of ever-growing renewable energy production, as well as the increasing dynamics of electricity demand in buildings. This paper provides a holistic review of (1) data-driven energy flexibility key performance indicators (KPIs) for buildings in the operational phase and (2) open datasets that can be used for testing energy flexibility KPIs. The review identifies a total of 48 data-driven energy flexibility KPIs from 87 recent and relevant publications. These KPIs were categorized and analyzed according to their type, complexity, scope, key stakeholders, data requirement, baseline requirement, resolution, and popularity. Moreover, 330 building datasets were collected and evaluated. Of those, 16 were deemed adequate to feature building performing demand response or building-to-grid (B2G) services. The DSM strategy, building scope, grid type, control strategy, needed data features, and usability of these selected 16 datasets were analyzed. This review reveals future opportunities to address limitations in the existing literature: (1) developing new data-driven methodologies to specifically evaluate different energy flexibility strategies and B2G services of existing buildings; (2) developing baseline-free KPIs that could be calculated from easily accessible building sensors and meter data; (3) devoting non-engineering efforts to promote building energy flexibility, standardizing data-driven energy flexibility quantification and verification processes; and (4) curating and analyzing datasets with proper description for energy flexibility assessm.

This paper describes a method to overcome current limitations in BIM software to design Building Automation and Control Systems (BACS) functions of Heating Ventilation and Air Conditioning (HVAC) systems through the extension of the IFC-files with information on control functions. The method is illustrated with the description of control functions for a use case including a boiler, a thermal storage, an air handling unit and a floor heating system modelled with a widely used BIM software tool. The focus is set on the description of three simple control functions: the definition of a heating curve, the setup of time schedules for the air handling unit and the flow temperature control for the heating circuit. The proposed method is based on available software tools linked together and enables adding complementary information on controls into the IFC-file, checking it for consistency, extracting and converting the IFC content into a linked data format. The use of semantic web technology allows for interoperability with various applications like e.g. software used for the commissioning or the supervision of HVAC systems and thus can contribute to reduce current quality and performance deficits of these systems.

The current development pace of the internet of things (IoT), digital controls, networks are exerting profound changes. However, much research had studied how IoT systems are technically applied, but more research is required to study the adaptation and standardization of applying IoT applications in architectural buildings. Moreover, there is a lack of approaches for evaluating IoT solutions to validate those applications in buildings. Besides, to link the gap between applying IoT solutions, engineering requirements, and the building resources’ impact. The main aim of this study is to propose a criteria framework to develop a simplified method, to assess IoT implementation level and the impact of IoT applications in buildings. This framework method is based on 3 main domains: input (embedded parameters), throughput (controlled variables), and outcome (impact) domains. Each domain collects a set of indicators and attributes with different IoT automation readiness levels. This framework contains 16 indicators and 124 attributes arranged in 5 main IoT levels. Then, we developed a proposed assessment method, the weights of this evaluation method are based on two verified rating systems that are used in evaluating the smart and green buildings. To make a preliminary theoretical validation of the proposed criteria framework, we applied it on four different selected international cases, that implemented IoT applications in their buildings. We could also identify which building is comparatively much in an advanced IoT level based on the overall score and level.

In the present energy scenario, buildings are playing more and more as energy prosumers. They can use and produce energy and also actively manage their energy demand. The energy flexibility quantifies their potential to adjust the energy demand on the basis of external requests. The objective of this paper is to propose a method for buildings energy flexibility labelling at design conditions in the same fashion as the energy performance label. The flexibility quantification is based on the calculation of four flexibility parameters, which contribute to the definition of the Flexibility Performance Indicator. In order to assess the Flexibility Performance Indicator, buildings dynamic simulations are necessary and the boundary conditions (i.e. demand response event, representative day, comfort constraints) to be considered during the evaluation are provided as part of the proposed methodology. The method was applied to different Italian buildings, which differ for geographic location and design specifications and, in particular, the effects of building structure, heating/cooling systems and energy storage systems were compared. Results show that the climatic conditions affect the flexibility performance, while the building feature more relevant is the thermal mass of the building envelope, more than that provided by the distribution system. A sensitivity analysis to evaluate how the results are influenced by the proposed boundary conditions was also performed. Their choice confirms to have a relevant impact on flexibility quantification, then their unique definition has a paramount importance within this methodology.

The Energy Performance of Buildings Directive 2018/844/EU introduced the smart readiness indicator (SRI) to provide a framework to evaluate and promote building smartness in Europe. In order to establish a methodological framework for the SRI calculation, two technical studies were launched, at the end of which a consolidated methodology to calculate the SRI of a building basing on a flexible and modular multicriteria assessment has been proposed. In this paper the authors applied the above-mentioned methodology to estimate the SRI of the Italian residential building stock in different scenarios. To this end, eight “smart building typologies”, representative of the Italian residential building stock, have been identified. For each smart building typology, the SRI was calculated in three scenarios: (a) base scenario (building stock as it is); (b) an “energy scenario” (simple energy retrofit) and (c) a “smart energy scenario” (energy retrofit from a smart perspective). It was therefore possible to estimate a national average SRI value of 5.0%, 15.7%, and 27.5% in the three above defined scenarios, respectively.

At the end of November 2016, the European Commission tabled the Clean Energy for All Europeans package, which represents a large set of proposals for several key directives related to energy. The package included proposed revisions to the Energy Performance of Buildings Directive (EPBD) which seek to update and streamline the Directive in several areas, including provisions to ensure buildings operate efficiently by encouraging the uptake of Information and Communication Technologies (ICT) and smart technologies. Although it can be argued that there is at present no commonly accepted definition of a “smart building”, the Commission’s proposed revision refers to three key features of a possible indicator of “smartness” in buildings: the technological readiness of a building to (1) interact with its occupants; (2) to interact with the grid; and (3) to manage itself efficiently. Using these three pillars of “smartness” as a methodological starting point, this paper identifies and analyses recent and ongoing Horizon 2020 research, innovation and market uptake projects which are investigating “smart buildings”. The research maps and examines the tasks, scope and innovations in areas that include building automation and control systems, demand response, energy management, ICT and user interfaces for energy efficiency.

Maritime transports are playing an increasing crucial role in the worldwide trade. The maritime ports are the core elements of these transport network. Any malfunctions in the ports could dramatically affect the performance of the entire network. Thus, a certain level of smartness or intelligence is required. In the same paradigm of smart cities, smart maritime networks are envisaged to make an optimal use of different resources, such spaces, machines, quays, energy resources, containers, etc. In this paper, we analyze the smartness level of some Moroccan ports. This study has been initialized, because Morocco is playing an increasing role as gate between Europe and Africa, and also as a hub for the world maritime transport. Smart maritime port is seen as a special smart environment case, categorized between smart cities and smart buildings. Therefore, the measure of the metric of smartness level is derived from the smart readiness indicators from these two special smart environments. Such indicator should serve as an instrument for the different players to defend their interests. For example, a national government could impose a certain level of smartness, where the environment protection and energy efficiency have more weight, and check how far the port authority protect the national interests. A mathematical model is used to generate numerical results for some local port use cases.

The Smart Readiness Indicator (SRI) is an assessment scheme for the intelligence of buildings, which was introduced by the European Commission in the directive for the Energy Performance of Buildings in 2018. Since its introduction, many activities related to the maturation and employment of the SRI have been initiated. One of the adaptation needs of the SRI, revealed through public consultation with relevant stakeholders, is the requirement for a tailored SRI for different types of buildings. The aim of this study is to analyze possible scenarios to optimize the smartness performance, as addressed by the SRI score, in educational buildings. The subject of this study concerned campus buildings of the Kaunas University of Technology, in Lithuania. For the definition of the SRI, the calculation sheet developed by the European Commission was used. The effect of the improvements in the smartness performance of buildings on their energy efficiency was examined with the use of a whole-building, BIM-based energy assessment tool (IDA-ICE). The findings of this study revealed that despite the improvement in the automation and control levels of the building heating system, the maximum SRI values achieved deviate significantly by a high-smartness level. This study revealed the importance of services at a city level towards achieving the optimal smartness levels at a building unit level. It also delivered useful findings related to the linkage between energy and smartness performance of a building. The policy implication of the study findings also covers topics relevant to utilities management at a district level, as well as on the need for tailored SRI services catalogs for different types of buildings.

To achieve the energy and emissions reduction goals for the building sector, actions are needed to improve energy efficiency and occupants’ wellbeing. To increase the uptake of smart technologies and the awareness upon their benefits, in line with the smart building revolution that is starting, the EPBD recast introduced the Smart Readiness Indicator (SRI) as a tool to evaluate the capability of buildings to easily adapt to both energy systems and occupants’ needs. However, there is a growing interest in studying the SRI features in terms of performance assessment, and, thus, dynamic simulation models can be exploited to better analyze its points of strength and weakness. The Energy Center building of Turin was chosen as case study. By means of EnergyPlus modeling, the current situation was simulated, as well as different scenarios of energy management and control, evaluating to what extent these actions can influence the overall SRI assessment. The analysis allowed to deepen and comment on the effectiveness of the SRI of being a real tool of building behavior assessment, able to link the indicator itself with the energy needs of the building and to understand if and how the indicator is sensible to energy needs variations.

In 2018, the revised Energy Performance of Buildings Directive (EPBD) included for the first time the application of a smart readiness indicator (SRI). Based on the fact that load shifting in and across buildings plays an increasingly important role to improve efficiency and alleviate the integration of renewable energy systems, the SRI is also aimed at providing an indication of how well buildings can interact with the energy grids. With the clustering of buildings into larger entities, synergies related to the integration of renewable energy and load shifting can be efficiently exploited. However, current proposals for the SRI focus mainly on qualitative appraisals of the smartness of buildings and do not include the wider context of the districts. Quantitative approaches that can be easily applied at an early planning stage are still mostly missing. To optimize infrastructure decisions on a larger scale, a quantifiable perspective beyond the building level is necessary to evaluate and leverage the larger load shifting capacities. This article builds on a previously published methodology for smart buildings with the aim to provide a numerical model-based approach on the assessment of whole districts based on their overall energy storage capacity, load shifting potential and their ability to actively interact with the energy grids. It also delivers the equivalent CO2 savings potential compared to a non-interactive system. The methodology is applied to theoretical use cases for validation. The results highlight that the proposed quantitative model can provide a meaningful and objective assessment of the load shifting potentials of smart districts.

In 2018, the European Commission adopted the Smart Readiness Indicator (SRI) concept in the recast of the directive on the energy efficiency of buildings. The set of SRIs is a measure of the intelligence of buildings systems, and its promotion is expected to contribute to the energy savings of the building sector. These indicators are relatively new and were developed only at the beginning of last decade, within European standards. This study introduces and elaborates on these indicators, as delivered in the final report of the European Commission. Some first results, which are obtained using a tool developed by the European Commission, are also presented. The work identifies gaps and perspectives for improvement of this system, as well as predicting the evolution of its implementation in the coming years, through specific numerical scenarios.

The increasing global energy demand, the foreseen reduction of available fossil fuels and the increasing evidence off global warming during the last decades have generated a high interest in renewable energy sources. However, renewable energy sources, such as wind and solar power, have an intrinsic variability that can seriously affect the stability of the energy system if they account for a high percentage of the total generation. The Energy Flexibility of buildings is commonly suggested as part of the solution to alleviate some of the upcoming challenges in the future demand-respond energy systems (electrical, district heating and gas grids). Buildings can supply flexibility services in different ways, e.g. utilization of thermal mass, adjustability of HVAC system use (e.g. heating/cooling/ventilation), charging of electric vehicles, and shifting of plug-loads. However, there is currently no overview or insight into how much Energy Flexibility different building may be able to offer to the future energy systems in the sense of avoiding excess energy production, increase the stability of the energy networks, minimize congestion problems, enhance the efficiency and cost effectiveness of the future energy networks. Therefore, there is a need for increasing knowledge on and demonstration of the Energy Flexibility buildings can provide to energy networks. At the same time, there is a need for identifying critical aspects and possible solutions to manage this Energy Flexibility, while maintaining the comfort of the occupants and minimizing the use of non-renewable energy. In this context, the IEA (International Energy Agency) EBC (Energy in Buildings and Communities program) Annex 67: “Energy Flexible Buildings” was started in 2015. The article presents the background and the work plan of IEA EBC Annex 67 as well as already obtained results. Annex 67 is a corporation between participants from 16 countries: Austria, Belgium, Canada, China, Denmark, Finland, France, Germany, Ireland, Italy, The Netherlands, Norway, Portugal, Spain, Switzerland and UK.

Traditional HVAC design process contains a large amount of time-consuming work such as load calculation for each space, hydraulic calculation to determine duct size and careful duct layout arrangement to avoid collision. Computer is more suitable for such kind of “dumb” work with higher efficiency and less fault. Also present building design work is a less integrated process where engineers of each domain focus on their own work. This often result in unconformity and poor quality with limited project time. IFC is a neutral platform, open file format specification to facilitate interoperability in the architecture, engineering and construction. In this paper, we propose a general framework of an IFC based integrated semi-automated design tool for central HVAC system design. It contains four main modules corresponding to specific design procedures. Human is allowed to make decision during the design process for more flexibility.

This study attempts to address a challenge regarding the extraction of building information, which is one of the fundamental tasks that needs to be addressed in the construction domain. Current technologies, such as relational databases, have difficulty in efficiently and effectively managing and querying the interconnected building information with full of hidden relationships. To address this problem, this study adopted the graph-theory-based graph database technology to reveal hidden relationships within building information. A model-driven approach was developed to enable a full conversion of Industry Foundation Classes data into labeled property graph, which is referred to as IFC-Graph. The result shows that IFC-Graph can represent interconnected building information and reveal hidden relationships, supporting effective and efficient building information access and query. This study can benefit a vast number of future studies in the area of building information query by improving its accessibility and queryability.

Non-residential buildings contribute to around 20% of the total energy consumed in Europe. This consumption continues to increase globally. Smart building proposals (focused on Nearly Zero Energy Building (NZEB), air quality monitoring, energy saving with thermal comfort, etc.) were already necessary before 2020, and the pandemic has made this research and development area more essential. Furthermore, the need to meet the Sustainable Development Goals (SDG) and obtain technological solutions based on the Internet of Things (IoT) requires holistic contributions through real installations that serve as spaces for measuring, testing, study and research. This article proposes a “measure–analyse–decide and act” methodology to quantify the Smart Readiness Indicator (SRI) for university buildings as a reference environment for energy efficiency and COVID-19 prevention models. Two conceptual spaces (physical and digital) within two dimensions (users and infrastructures) are designated over an IoT three-level model (information acquisition, interoperable communication, and data-driven decision). An IoT ecosystem (sensoriZAR) was implemented as a proof-of-concept of a smart campus at the University of Zaragoza, Spain. Focused on CO2 and energy consumption monitoring, the results showed effectiveness through real installations, demonstrating the IoT potential as SDG-enabling technologies. These contributions allow not only experimental lab tests (from the authors’ expertise in several specialties of Industrial, Mechanical, Design, Thermal, Electrical, Electronic, Computer and Telecommunication Engineering) but also a reference model for direct application in academic works, research projects and institutional initiatives, extendable to professional environments, buildings and cities.

Decarbonizing the building sector by means of the smart interconnection of increasingly efficient buildings is one of the European Union's goals in order to meet its climate and energy targets and a milestone in delivering future positive-energy cities. Energy performance certificates today, and smart readiness indicators tomorrow should positively influence consumers’ choices and orient them towards an appreciation of sustainable buildings. Although energy performance certificates are now required by law in the EU Member States, it remains to be seen whether and how local real estate markets react to them. To fill this gap, we analyzed a cross-sectional housing dataset in the Italian city of Bolzano, which serves as a noteworthy case study due to its compact urban form and the environmental awareness of its local society. We first tested a hedonic price model, and then its spatial specification. Considering the certified energy efficiency performance and the characteristics of the location among the explanatory variables, we found a price premium in excess of 6% on moving from the worst (“G”) to the best (“A”) energy efficiency class, all other characteristics being equal. The presence of a spillover effect to nearby properties can also be framed as an additional co-benefit of retrofitting.

The ongoing energy system shift—from traditional centralized fossil fuel based to decentralized renewable energy sources based—requires a strengthened control of energy matching. Smart buildings represent the latest step in building energy evolution and perform as active participants in the cluster/energy infrastructure scale, becoming energy prosumers. In this framework, the IEA EBC Annex 67 introduces the concept of ‘Energy Flexible Building’, defined as a building able to manage its demand and generation in accordance with local climate conditions, user needs and grid requirements. Currently, there is no insight into how much flexibility a building may offer, and this study aims to overview the theoretical approaches and existing indicators to evaluate the Energy Flexibility of building clusters. The focus on cluster scale allows for the exploitation of the variation in energy consumption patterns between different types of buildings and the coordination of load shifting for the improvement of renewable energy use. The reviewed indicators can contribute to the definition of the Smart Readiness Indicator, introduced in the European Commission proposal for the EPBD revision, in order to test a building’s technological readiness to adapt to the needs of the occupants and the energy environment, as well as to operate more efficiently.

In the current context of joint efforts towards the decarbonisation of buildings, integrating occupants’ comfort and health with latest technological advancements for energy efficiency is at the center of the latest development of research, policies and professional practice. Radiant systems are encountering great success since the low-thickness systems can also be used in renovation projects for both heating and cooling, while guaranteeing optimal comfort. However, dehumidification is often required for optimal radiant cooling operation with no condensation risks, and the great potential of mechanical ventilation systems to optimally address the needs for dehumidification, air renewal, health and energy efficiency appears to be far from its full exploitation in the post-COVID-19 era. The present paper aims at providing a quantification of the energy and financial impacts of the implementation of a controlled mechanical ventilation system (CMV) coupled to a radiant system in a typical residential case study building in Italy. The results show that the sole CMV may decrease primary energy demand and energy costs by more than 30% and contribute to an increase in the smart readiness of the building by 8%, but further incentive policies must be developed to cover the still high investment and maintenance cost.

Building Automation and Control Systems (BACS) offer promising opportunities to reduce building energy consumption, aligning with the European Union's climate goals. This review critically evaluates the accuracy of energy savings estimation in BACS implementation, with a specific focus on the European standard EN 52120–1. BACS enable effective control of various building services, including heating, cooling, ventilation, lighting, and shading, thereby improving the energy demand-supply balance while maintaining or improving comfort. The review examines energy savings reported in the literature, encompassing field studies and dynamic energy performance simulations, to assess the validity of EN 52120–1's proposed factors. Findings indicate that the factors outlined in EN 52120–1 are not sufficiently accurate in estimating energy savings from BACS implementation. Critical parameters such as occupancy rate, climate conditions, building orientation, occupancy behavior, and control algorithms are often overlooked but significantly influence BACS effectiveness. Additionally, many studies present oversimplified reference cases, leading to overestimated energy savings. While improvements in simulation parameters are necessary, building energy performance simulations appear to be the most reliable method for accurately assessing BACS savings during the design phase. By considering the complexities of building characteristics, climate models and occupant behavior, simulations provide a comprehensive evaluation of BACS performance. This review highlights the limitations of EN 52120–1's factors and emphasizes the importance of considering crucial parameters for accurate estimation of energy savings in BACS implementation. It underscores the need for comprehensive and realistic approaches to assess the energy efficiency potential of BACS in buildings, taking into account various influencing factors.

Renewable energy is the energy that makes use of the continuous natural processes for its production and renews itself in a shorter time than the depletion rate of the resources it uses for production. The types of renewable energies include geothermal energy, wind energy, solar energy, hydroelectric, hydrogen, wave and biomass energy. Zero energy buildings (ZEB or nZEB) are highly energy efficient buildings with zero net energy consumption, meaning that the total amount of energy used by the building on an annual basis is equal to the amount of renewable energy created on the site or by renewable energy sources offsite, using technologies such as heat pumps, high efficiency windows and insulation, and solar panels. This definition is also used by the European Parliament Building Energy Performance Directive (EPBD) [Directive (EU) 2010/31/EU]. The directive enforces the buildings built after December 31, 2020 to be zero-energy buildings (ZEB) or nearly zero energy buildings (nZEB). They should be cooled or heated according to their purpose by renewable sources. The purpose of calculating energy performance in buildings is to determine the annual total energy demand given in net primary energy corresponding to energy for heating, cooling, ventilation, hot water and lighting. Therefore, high-energy consuming buildings should be supported with renewable energy. For residential buildings, most Member States aim to have a primary energy use of no higher than 50 kWh/(m2 y). In our country, the energy performance of buildings is determined using the calculation method within the scope of the National Energy Performance of Buildings Method and using BEP-TR software. The method followed in this study is based on BEP-TR software and calculations. The Turkish Standards Institute study method (TSE/TSI) begins with the determination of the reference specifications for each building type, using the data. The share of renewable energies in the total energy supply is required for the net zero energy building concept, taking into account active systems such as photovoltaic panels, hot water collectors and heat pumps. As a result, net zero energy buildings, supported by renewable energy, have begun to be implemented in Turkey. The location of the buildings, the number and density of the building occupants provide a useful flexibility to reduce the performance deficiencies that may be experienced due to the design features and to achieve the nZEB targets. The European Union took the first step with the Energy Performance in Buildings Directive (2002/91/EC), EPBD. The directive, which was revised in 2010 (2010/31/EU), introduced concepts such as ``reference building'', ``optimum cost'' and ``nearly zero energy buildings''. The last revision of EPBD was approved in 2018. The new revision includes the strengthening of indoor environment quality, proper maintenance and effective inspection and setting more ambitious energy efficiency targets in line with the opinions of the stakeholders and REHVA. Encouraging the use of information and communication technology (ICT) and smart technologies (smart meters, building automation and control systems) to ensure efficient operation of buildings, energy storage and the definition of ``smart readiness indicator'' that shows how ready the buildings are for compliance with the distribution network, requests the renovation of existing and old buildings. The European Commission proposed a revision of the directive (COM (2021) 802 final) in 2021. It upgrades the existing regulatory framework to reflect higher ambitions and more pressure on climate and social action, while providing EU countries with the flexibility needed to take into account the differences in the building stock across Europe. Digitalization is a good opportunity to increase the share of various renewable energy sources to meet the demand for heating and cooling. Approximately 19% of Europe's heating and cooling consumption is met by renewable energy (mostly solid biomass) (EEA 2018). Renewable energy technologies used to heat and cool buildings can be placed in individual units of small capacity or in DHC, district heating and cooling systems with larger capacities. Digitization, by optimizing implementation, planning and business models, reduces the total cost of decarbonization by connecting heat and cooling device manufacturers, users, local stakeholders and energy markets. It is a driving force for smart buildings, smart communities, smart cities, local energy and district heating and cooling (DHC). In many buildings today, control is limited to at most one room thermostat. Even though thermostats are programmable, many building occupants do not know their existence or do not know how to do it. The benefits of digitization for heating and cooling, even the existence of technologies, are little known. However, heating and cooling are vital for comfort at home and at work. Using devices that analyze and process large amounts of data, digital technologies provide a new data layer that can be energetically and socially utilized, helping to better manage the building energy system and increase energy efficiency. For example, when digitization and electrification are used together, direct communication between the building and the main grid can be achieved, and both generation and demand sides can be optimized through some innovative approaches. ``Internet of Things'' solutions create greater interaction between HVAC systems and building occupants; consumers can become more aware of energy waste and make their own energy choices more consciously. Advanced HVAC technologies are actually ready for this environment; the main challenge is to show that they are economically and financially sustainable through cost–benefit analysis.

Currently, no common definition of PEBs exists, which complicates technical solutions' and business models' development across different climatic zones. This chapter defines a PEB based on thorough research conducted in the EU-funded EXCESS project. The chapter begins with a discussion on PEB backgrounds, such as PEB concepts' origin and comparison of concepts similar to PEB. After that a technical framework is introduced by presenting different technical criteria that need to be considered. Based on the EXCESS project's research, a positive energy building (PEB) is defined as `an energy-efficient building that produces more energy than it uses via renewable sources, with high self-consumption rate and high energy flexibility, over a time span of one year'. After the technical framework introduction, a social framework of PEBs is discussed. Finally, a technically and socially feasible PEB definition is provided. This discussion includes PEB as part of an energy community and PEBs in relation to smart readiness indicator.

The reduction of energy demand in buildings through the adoption of energy efficiency policy is a key pillar of the European Union (EU) climate and energy strategy. Energy efficiency first emerged in the EU energy policy agenda in the 1970s and was progressively transformed with shifting global and EU energy and climate policies and priorities. The paper offers a review of EU energy policies spanning over the last half century with a focus on policy instruments to encourage measures on energy efficiency in new and existing buildings. Starting from early policies set by the EU in response to the Oil Embargo in the 1973, the paper discusses the impact of EU policies in stimulating energy efficiency improvements in the building sector ranging from the SAVE Directive to the recently 2018 updated Energy Performance of Buildings Directive and Energy Efficiency Directive. The review explores the progress made over the last 50 years in addressing energy efficiency in buildings and highlights successes as well as remaining challenges. It discusses the impact of political priorities in reshaping how energy efficiency is addressed by EU policymakers, leading to a holistic approach to buildings, and provides insights and suggestions on how to further exploit the EU potential to save energy from buildings.

The building sector accounts for 40% of the total energy consumption in the EU. It faces great challenges to meet the goal of transforming the existing building stocks into near zero-energy buildings by 2050. The development of Energy Performance Certificate (EPC) schemes in the EU provides a powerful and comprehensive information tool to quantitatively predict annual energy demand from the building stock, creating a demand-driven market for energy-effective buildings. Properties with improved energy rating have had a positive impact on property investments and rental return because of the reduced energy bills. In addition, the EPC databases have been applied to energy planning and building renovations. However, it should be mentioned that the current evaluation system faces problems, such as not being fully implemented, delivering low quality and insufficient information to stimulate renovation, therefore requiring improvements to be made. This paper provides a review of the current EPC situations in the EU and discusses the direction of future improvements. The next generation EPC should rely on BIM technology, benefit from big data techniques and use building smart-readiness indicators to create a more reliable, affordable, comprehensive and customer-tailored instrument, which could better represent energy efficiency, together with occupants’ perceived comfort, and air quality. Improved EPC schemes are expected to play an active role in monitoring building performance, future energy planning and quantifying building renovation rates, promoting energy conservation and sustainability.

This study aims to improve data exchange between building information modelling (BIM) and building energy modelling (BEM) tools to aid HVAC engineers in applying rightsizing methods. An ontology-driven common data environment (CDE) is developed consisting of a centralized repository and four tools: a BIM model, a hydraulic calculation engine, a whole-building simulation engine and a data visualization tool. The study uses a primary school building in Denmark within a demonstration environment and evaluates the impact of rightsizing methods on indoor climate, material consumption, and energy consumption. The demonstration environment showcases the effectiveness of an ontology-driven common data environment in representing and managing heterogeneous building information throughout the HVAC design process. However, the study has limitations, such as only focusing on the ventilation system of an already-constructed building, not considering other HVAC systems, and using only one building. Further studies are needed to generalize the findings and consider factors such as user behaviour and energy sources.

Smart buildings and smart cities are not the future perspectives anymore—the smart building integration into a smart city is an actual question for today and tomorrow. Development of smart buildings not only enhances the smart city concept but also promotes positivity to the urban development and national economy, and increases the quality of life of the whole population reacting to global challenges of sustainability. The innovative smart building and smart city technologies enable us to overcome these challenges by being employed through all real estate (RE) project development stages. The Evaluation Framework for Real Estate Development in Smart Cities created by the authors provides the possibility to assess the existing as well as to forecast future RE projects integration into a smart city during the whole life-cycle stage. The practical application of the presented evaluation framework was illustrated by the comparative case study. Based on the created smart building integration into a smart city evaluation framework for real estate development, 10 RE projects in Lithuania and over the world were assessed and rated by selected criteria relevant to different RE development stages. The evaluation results revealed that, especially at the design and construction stages, the existing intelligence of RE projects and/or cities is insufficient. Although real estate projects are technologically advanced as single entities, the integration into smart city networks is limited by interoperability capabilities of the cities or by different strategic goals settled by real estate developers.

The concept of Smart Buildings was introduced by the Energy Performance Building Directive, with the aim to promote energy flexibility, renewable energy production and user interaction. A wide range of definitions have been introduced in the literature to characterize smart buildings yet, at present, its’ concept and features are not clearly and uniquely defined. Simultaneously, building energy retrofit concept has been introduced to facilitate achieving the nearly Zero-Energy Building target and reduce energy consumption in existing buildings. Up to 90 % of the existing European building stock will still be standing and in use in 2050. Thus, there is a need to upgrade the existing retrofitting strategies into Smart Retrofitting, to achieve the nearly Zero Energy Building target and be able to respond to external dynamic conditions such as the weather and the grid. The aim of this research is first to review the concept of smartness in the built environment, highlighting the main features, functions, and technologies of smart buildings, also discussing the possible challenges for smart retrofit applications. The second part of the paper reviews the existing Key Performance Indicators that measure the performance and success in achieving goals in smart buildings. The need to develop a quantified guideline to improve energy and technological innovation is the basis for the increase of the smartness in buildings. Consequently, a set of nine groups of representative performance indicators for smart buildings is developed. This work shows current gaps in the literature and highlights the space for foreseeable future research.

Subject of this study is the analysis of the Smart Readiness Indicator (SRI), as well as its application for a residential building in Greece. The indicator, which was firstly introduced in the revised EPBD in 2018, assesses the buildings’ smart readiness through the examination of the presence and the evaluation of the functionality level of smart services. Its goal is the promotion of buildings that are energy efficient, adaptive to their users’ needs, and flexible in respect of their electricity demand, according to the three key - functionalities, as stated by the Directive. A smart building is not only characterized by its sustainability but also by its adaptiveness to the environmental conditions and its users’ preferences. Smart buildings are a basic component of smart cities, which utilize a great range of smart technologies aiming at the improvement of their citizens’ lives. The Smart Readiness Indicator as well as the sub indicators evaluate the smart buildings using a multicriteria assessment method, which is thoroughly described in this paper. Finally, the indicators’ calculation is executed for a residential building in Greece leading to results, which are discussed along with identified methodology shortcomings and difficulties.

The current research applies the SRI methodology in two typologies of typical residential buildings, Single-Family Houses and Multi-Family Houses, in five EU Countries, to evaluate the retrofitting cost towards buildings smartification and assess the SRI score when different retrofitting scenarios are applied. To that end, a three-step assessment process is adopted. First, the SRI is calculated for the baseline scenario representing the national minimum requirements according to the EPBD. Next, the SRI is calculated after applying a retrofitting scenario that includes market available technologies towards Nearly Zero Energy Buildings. Last, a more comprehensive retrofitting scenario of integrated technologies towards Positive Energy Buildings is assessed. Results indicate that buildings, constructed after the implementation of the EPBD, can increase smartness with a relatively low cost than older buildings, although their initial overall SRI score generally leads to an SRI Class G (0–20%), with buildings performing better in “Health, well-being and accessibility” and “Comfort” impact categories. Smart-orientated retrofitting scenarios focusing on building automation and control measures can increase such buildings class up to “C” (65–80%), performing better in optimizing energy efficiency when applying retrofits towards NZEB. Applying retrofitting scenarios that could potentially lead to energy positiveness mainly supports building interaction with the grid.

Buildings have now adopted a new dimension: the dimension of smartness. The rapid arrival of connected devices, together with the smart features that they provide, has allowed for the transition of existing buildings towards smart buildings. The assessment of the smartness of the large number of existing buildings could exhaust resources, but some organisations are requesting this regardless (such as the smart readiness indicator of the European Union). To tackle this issue, this work describes a tool that was created to find connected devices to automatically evaluate smartness. The tool, which was given the name SmartWatcher, uses a design-for-purpose natural language processing algorithm that converts verbal information into numerical information. The method was tested on real buildings in four different geographical locations. SmartWatcher is shown to be powerful, as it was capable of obtaining numerical values from verbal descriptions of devices. Additionally, a preliminary comparison of values obtained using the automatic engine and clipboard assessments showed that although the results were still far from being perfect, some visual correlation could be seen. This anticipates that, with the addition of appropriate techniques that refine this algorithm, or with the addition of new ones (with other more advanced natural language processing methods), the accuracy of this tool could be greatly increased.

With the third revision of the Energy Performance of Buildings Directive (EPBD) issued in July 2018, the assessment of buildings now has to include a Smart Readiness Indicator (SRI) to consider the fact that buildings must play an active role within the context of an intelligent energy system. In order to support the development of the SRI, this article describes a methodology for a simplified quantitative assessment of the load shifting potential of buildings. The aim of the methodology is to provide a numerical, model-based approach, which allows buildings to be categorized based on their energy storage capacity, load shifting potential and their subsequent interaction with the grid. A key aspect is the applicability within the Energy Performance Certificate (EPC) in order to provide an easy to use calculation, which is applied in addition to the already established energy efficiency, building services and renewable energy assessments. The developed methodology is being applied to theoretical use cases to validate the approach. The results show that a simplified model can provide an adequate framework for a quantitative assessment for the Smart Readiness Indicator.

The increased smartness of the built environment is expected to contribute positively to climate change mitigation through energy conservation, efficient renewable energy utilization, and greenhouse gas emission reduction. Accordingly, significant investments are required in smart technologies, which enable the distributed supply of renewables and increased demand-side energy flexibility. The present study set out to understand the cash flows and economic viability of a real-life smart system investment in a building. The data collection process was threefold: First, a case building’s level of (energy) smartness was estimated. Second, the semi-structured interviews were held to understand the building owner’s motives for a smart investment. Third, the investment’s profitability was analyzed. The study found that the progressive smartness investment was technically feasible, and surprisingly also economically profitable. The original EUR 6 million investment provided over 10% return-on-investment and, thus, increased the property value by more than EUR 10 million. Moreover, the commercial partners also emphasized the strategic value gained by renewable energy and environmental performance. The high level of smartness with a good return on investment was accomplished mainly through new income generated from the reserve power markets. However, the results implied that financial profitability alone was not enough to justify the economic viability of a smart building system investment.

Well-designed and properly implemented Building Automation and Control Systems (BACS) can contribute to a reduction of the energy consumption in buildings, while increasing comfort and convenience for the occupants. For design and planning purposes, there is a need to quantify the potential impacts of implementing BACS, especially related to their capability for reducing the operational energy demand of a building. The simplified BAC factor method defined in standard EN 15232 aims to provide a generic estimation of expected energy savings. Alternatively, dynamic energy performance simulations can provide more detailed insights on a particular building design. Comparing energy savings from BACS in different sources in literature reveals significant discrepancies between various studies and assessment methods. This paper aims to clarify and discuss the differences between the various assessments and to identify the parameters that could affect BACS (i.e. heating, domestic hot water supply, lighting and shading control systems) performance in residential buildings. It is concluded that simplified methods as the EN 15232 BAC factor method do not provide a reliable estimate of achievable energy savings. The results obtained by more detailed simulations reported in literature show a significant variation in BACS performance. Two main causes are identified. Factors such as building and installation design parameters, occupant behaviour, context (e.g. climate) and baseline energy demand affect the energy saving potential but are not explicitly taken into account in the BAC factor method. Next, a significant part of the variation in reported energy saving potential can be attributed to discrepancies in modelling methods.

Three-dimensional (3-D) geometry can be described in many ways, with both a varying syntax and a varying semantics. As a result, several very diverse schemas and file formats can be deployed to describe geometry, depending on the application domain in question. In a multidisciplinary domain such as the domain of architecture, engineering, and construction, this range of specialized schemas makes file format conversions inevitable. The approach adopted by current conversion tools, however, often results in a loss of information, most often due to a “mistranslation” between different syntaxes and/or semantics, leading to errors and limitations in the design conception stage and to inefficiency due to the required remodeling efforts. An approach based on semantic web technology may reduce the loss of information significantly, leading to an improved processing of 3-D information and hence to an improved design practice in the architecture, engineering, and construction domain. This paper documents our investigation of the nature of this 3-D information conversion problem and how it may be encompassed using semantic web technology. In an exploratory double test case, we show how the specific deployment of semantic rule languages and an appropriate inference engine are to be adopted to improve this 3-D information exchange. It shows how semantic web technology allows the coexistence of diverse descriptions of the same 3-D information, interlinked through explicit conversion rules. Although only a simple example is used to document the process, and a more in-depth investigation is needed, the initial results indicate the suggested approach to be a useful alternative approach to obtain an improved 3-D information exchange.

One of the main challenges of automated compliance checking systems is aligning the semantics of the building information models (BIMs), in Industry Foundation Classes (IFC) format, and the semantics of the regulations, in natural language, to allow for checking the compliance of the BIM with the regulations. Existing information alignment methods typically require intensive manual effort and their ability to deal with the complex regulatory concepts in the regulations is limited. To address this gap, this paper proposes a deep learning method for IFC-regulation semantic information alignment. The proposed method uses a relation classification model to relate and align the IFC and regulatory concepts. The method uses a transformer-based model and leverages the definitions of the concepts and an IFC knowledge graph to provide additional contextual information and knowledge for improved classification and alignment. The proposed method was evaluated on IFC concepts from IFC 4 and regulatory concepts from different building codes and standards. The experimental results showed good information alignment performance.

Existing model checkers of building information modeling (BIM) can open industry foundation classes (IFC) files and perform clash detection between mechanical, electrical, and plumbing (MEP) and architectural as well as structural models. However, there are few applications of IFC data in MEP engineering, specifically in interactive visualization between calculation results and BIM models. This study focuses on the systematic analysis of a BIM model and interactive visualization of IFC data for MEP engineering. First, the systematic analysis method for a ventilation system is proposed using the critical path class. Second, referring to the IFC relationship of connections between duct elements, elements in the same system are sorted into different levels of critical paths. Furthermore, ifcPressureViewer is developed based on the interactive visualization methodology by connecting Three.js, a JavaScript 3D library, with Echarts, a JavaScript visualization library, which is applicable for desktop computers and mobile tablets. This is followed by a case study that demonstrates the application of the interactive visualization process. Finally, the conclusions of this study are summarized, and directions for future research are discussed.