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[Display omitted] • Review research trends and examines environmental performance issues related to social housing. • The findings indicate the main issues related to the environmental performance of social housing. • The content analysis identifies the co-occurrence of these identified issues and the frequency of the co-occurrence. • Examines the trends in terms of the focus areas and design/operation factors investigated by country of origin. Social housing is essential in addressing the housing needs of vulnerable people. However, there are environmental performance-related issues to consider with respect to social housing, and limited effort has been directed toward consolidating environmental performance outcomes to inform social housing design. In this context, the research presented in this paper aims to review research trends in this domain and examines environmental performance issues and opportunities related to social housing. A mixed method systematic review, including bibliometric and content analysis, is employed to uncover the critical environmental performance patterns and associated design factors for social housing. A total of 230 articles were selected for bibliometric analysis to identify the key issues related to the environmental performance of social housing. Among them, 83 highly focused articles are selected for content analysis, exploring the detailed issues and design factors related to the environmental performance of social housing. The research findings indicate that indoor air quality, thermal comfort, energy efficiency, and affordability are the main issues related to the environmental performance of social housing. The content analysis also identifies the co-occurrence of these identified issues and the frequency of the co-occurrence. Furthermore, this paper examines the trends in terms of the focus areas and design/operation factors investigated by country of origin and reveals that: (1) North America and Europe contributed a substantial share of their research on indoor air quality. (2) Thermal comfort merged as the predominant focus consistently studied in conjunction with energy efficiency across various regions. In addition, the key design and operation factors associated with these issues are identified. This paper provides novel insights into the environmental performance of social housing and associated design and operation factors. [ABSTRACT FROM AUTHOR]

Initiatives aimed at retrofitting the existing buildings stock and constructing Zero Carbon Community (ZCC) have been recognized as highly effective measures for reaching the zero emission carbon goals set by the EU Commission by 2050. The Building Energy Modeling approach focuses only on typical individual building and it is hardly suitable for assessing the performance and the mutual interaction of a group of interconnected buildings In this case the application of Urban Building Energy Modeling (UBEM) is required. Research on UBEM constitutes the foundation for reducing the energy needs of the built environment; however, there is currently a lack of knowledge on UBEM concept, workflow and related simulation tools. This paper has twofold objectives; first presents an in-depth bibliometric analysis, through CiteSpace, to analyze the developmental trajectory of emerging simulation UBEM method. The analysis encompasses the distribution of authors collaborations, geographic and institutional cooperation in publications and research hotspots. Based on this, the paper summarizes the development trends and potential directions for research in the UBEM field. Furthermore, as a second objective, the paper provides a comprehensive analysis of the main UBEM tools with focus on the input and output data, simulation engine and data workflow. The final scope of this study is to offer to urban planners and policymakers a comprehensive framework as a starting point for approaching zero-carbon communities' from both the concepts and the simulations analysis point of view. [ABSTRACT FROM AUTHOR]

Positive energy districts and sustainable plus energy neighbourhoods are developed in the European context to reduce energy use and greenhouse gas emissions from the building sector. The planning and development of positive energy districts and sustainable plus energy neighbourhoods is complex and requires collaboration between stakeholders and new measures to achieve high energy efficiency, local renewable energy generation, energy storage and flexibility, and energy sufficiency. This paper examines the implementation of energy measures in the planning and design of four positive energy district and neighbourhood development projects in Norway and Sweden. The paper compares the two different institutional and energy system contexts and how these affect the development of positive energy districts, focusing on the perspectives of the municipality and developers. Existing academic literature and positive energy district guidelines are used to develop an analytical framework for the planning and design of positive energy districts and sustainable plus energy neighbourhoods. Results highlight an early focus on energy ambitions, wide stakeholder involvement, and the importance of aligning interests between stakeholders and working interdisciplinary in the planning and design phases to find optimal energy measures. Both the building and the neighbourhood/district level are important to increase energy efficiency, energy sufficiency, and energy flexibility, and consequently lower the environmental impact of the whole development project. [ABSTRACT FROM AUTHOR]

[Display omitted] • Social Network Analysis and Text Mining to analyze 8024 abstracts on LCA and Building. • The semantic brand score measures the semantic importance of seven issues related to Building and Sustainability. • Limited publications assessing the social impact of LCA in buildings. • Materials are the most central issue in the literature on sustainable buildings. Research on Life Cycle Assessment (LCA) is being conducted in various sectors, from analyzing building materials and components to comprehensive evaluations of entire structures. However, reviews of the existing literature have been unable to provide a comprehensive overview of research in this field, leaving scholars without a definitive guideline for future investigations. This paper aims to fill this gap, mapping more than twenty years of research. Using an innovative methodology that combines social network analysis and text mining, the paper examined 8024 scientific abstracts. The authors identified seven key thematic groups, building and sustainability clusters (BSCs). To assess their significance in the broader discourse on building and sustainability, the semantic brand score (SBS) indicator was applied. Additionally, t building and sustainability trends were tracked, focusing on the LCA concept. The major research topics mainly relate to building materials and energy efficiency. In addition to presenting an innovative approach to reviewing extensive literature domains, the article also provides insights into emerging and underdeveloped themes, outlining crucial future research directions. [ABSTRACT FROM AUTHOR]

• An integrative literature review was carried out, with 269 papers reviewed. • Italy publishes the most on the subject, followed by China, Spain, and the USA. • Improving the envelope's thermal insulation is the most used strategy worldwide. • Other aspects of retrofits were discussed, including benefits and disadvantages. • Future retrofit assessments were suggested based on the conclusions. Given the high energy consumption and greenhouse gas emissions associated with buildings worldwide, sustainable strategies that provide conscious use of natural resources and suitable conditions of indoor environments for users are essential. In this sense, the objective of this paper was to present an overview of retrofit strategies aimed at improving energy efficiency and sustainability in buildings. The work is based on an integrative literature review of studies carried out in several countries over the last seven years. The main benefits of the retrofit strategies are reducing energy consumption, decreasing pollutant emissions, and improving the indoor environmental quality for the occupants. Active retrofit strategies are related to building systems and renewable energy sources, while passive strategies use natural resources more efficiently and generally at a lower cost. Methods and software can support the selection of the best retrofit strategies for each building. This paper compared and discussed the relevant results of the case studies, which comprised 72.9% of the papers selected for the integrative review. In all types of buildings and climates, the most discussed retrofit strategies were building envelope insulation, improving the climatisation and lighting systems, and using renewable energy sources – representing approximately 70% of the strategies. There was also a significant frequency of optimisation of natural lighting, natural ventilation, and ventilated façades in non-residential buildings or other types, mainly in semi-arid, temperate, and Mediterranean climates. Finally, recommendations for future studies were presented. [ABSTRACT FROM AUTHOR]

[Display omitted] • Insights from hydronic heating systems in 259 commercial buildings. • Systems typically operate 81% of hours annually, far more frequently than expected. • Loads are very low relative to equipment capacity, causing inefficient operation. • Typical condensing boilers spend few hours at low return temp., reducing efficiency. • Paper includes an open-source dataset of 216 buildings and associated code. This paper analyzes hydronic heating systems in 259 buildings across 56 US organizations. The dataset comprises 120,000,000 measurements taken by building automation systems from 2014 to 2024 and for a typical building contains measured supply and return water temperature, flow rate, output power, system state and outdoor temperature spanning 2.2 years (15-minute interval). Pump and boiler data are available for smaller subsets of buildings. Analysis shows that many assumptions about these systems do not align with real-world performance. Systems operate far more frequently than expected − 81% of hours annually for the median building. Loads are lower and more skewed than expected, and oversizing is common even accounting for redundancy requirements. Load distributions indicate that relatively small equipment could serve a surprisingly large percentage of annual loads. Notably, equipment sized to 30% of the maximum measured load could serve up 84% of the total heating energy consumption for the median building. Analysis of key parameters, such as supply water temperature, show that functional resets are rare and that manual overrides are common, often persisting for months or years. Return water temperatures and other data show that boilers operate less efficiently than expected. These findings offer valuable insights for operators, designers, owners, and policymakers, highlighting that substantial energy savings opportunities are commonplace and can be captured by modifying design and operation based on how these systems actually operate. The paper includes the code used for analysis and visualization as well as an open-source dataset of 216 buildings. [ABSTRACT FROM AUTHOR]

• This study pioneers the use of deep learning in transforming Measurement and Verification (M&V) within industrial buildings, significantly curtailing the reliance on prolonged data collection periods, and setting a new standard for efficient energy management. • Our findings elucidate the impact of dataset size on M&V accuracy, showcasing that a compact, four-month dataset is sufficient for dependable energy savings verification, challenging the traditional norms of lengthy data collection. • Through a rigorous comparison of twelve methods, this paper underscores the superior performance of LASSO regression, Sequential Feature Selector, and Recursive Feature Elimination in refining model accuracy, marking a significant step forward in feature selection methodology. • Investigating various configurations, this research identifies five hidden layers as the ideal architecture for deep neural networks, optimizing the balance between model complexity and predictive precision in energy savings. • The study significantly contributes to the acceleration of the M&V process, enabling more rapid decision-making and implementation of energy conservation measures, thereby improving operational efficiency in industrial environments. • Highlighting the extensive implications for sustainable practices, this research illustrates how the integration of sophisticated analytics and deep learning can revolutionize energy management strategies, promoting a more sustainable approach to building operations. This study explores the potential of advancing industrial building energy Measurement and Verification (M&V) using Deep Learning techniques, with a focus on the impact of data size and feature selection methods on model performance. Traditional M&V practices in the industry require extensive datasets, typically spanning 24 months, to ensure accurate energy savings verification post energy conservation measures (ECMs). This research challenges the norm by investigating whether a condensed four-month dataset could suffice for reliable M&V, thereby accelerating the verification process which is crucial for industrial applications. Utilizing a dataset with 30-minute intervals, the study first applies multi-linear regression across twelve feature selection methods to identify the most effective techniques based on a comprehensive set of performance metrics including Training Time, RMSE, MAE, R-squared, CVRMSE, NMBE, Mean MSE (via k-fold cross-validation), and the Standard Deviation of MSE (over 10-fold cross-validation). The top three methods—LASSO regression, Sequential Feature Selector, and Recursive Feature Elimination—were further analyzed as input features for a Deep Neural Network (DNN) model. The DNN's performance was evaluated across varying data sizes (20 %, 40 %, 50 %, 60 %, 80 %, and 100 %) and configurations ranging from one to ten hidden layers. The findings reveal that LASSO regression, when integrated with DNN, consistently outperforms in terms of CVRMSE and NMBE metrics across different data sizes and model complexities. Notably, increasing the dataset size from 20 % to 80 % markedly improves the model's predictive accuracy, underscoring the significance of larger datasets in enhancing DNN generalization and performance. Additionally, the study highlights a critical trade-off in DNN architecture: while models with fewer hidden layers (1–3) show greater performance variability in smaller datasets, increasing the number of layers does not linearly translate to better performance, illustrating the nuanced balance between model complexity and efficacy. This research contributes to the energy M&V field by demonstrating that shorter duration datasets, previously considered insufficient, can indeed provide accurate energy savings verification when analyzed with advanced deep learning techniques. This advancement not only paves the way for quicker, more efficient M&V processes in industrial settings but also offers valuable insights into the optimization of DNN models for energy data analysis. [ABSTRACT FROM AUTHOR]

• The impact of insulation moisture ingress and thermal bypass are investigated. • 2D computational fluid dynamics is used to simulate thermal bypass in buildings. • Moisture ingress can increase thermal transmittance by a factor of ∼2.2. • Thermal bypass can increase thermal transmittance by a factor of ∼3.5. • Insulation detailing and installation can significantly impact heat loss in buildings. Improving the performance of buildings is a key part of the global effort to limit greenhouse gas emissions and reduce energy consumption. Reducing the designed thermal transmittance of building envelopes through the addition of insulation is one of the primary measures adopted in many nations aiming to address the building sector. In theory, these globally adopted measures to improve design should result in considerable energy savings. The reality however is different. And a mismatch between design and in-use performance has been widely cited. This performance gap is extensively attributed to changes in occupant behaviour – the rebound effect. Considerably less research has investigated technical performance gap rationale. This paper aims to address this literature gap by investigating two technical reasons why design and in-use performance might differ; namely: i) moisture presence in the building's insulation and ii) thermal bypass of the insulation system. The study uses a combination of thermal resistance analogies and computational fluid dynamics, to ascertain the extent to which compromised insulation would impact the performance of building envelopes. An increase in envelope heat loss results in an increase in a building's energy consumption, and hence its associated greenhouse gas emissions. The study concluded that, in extreme cases, the presence of moisture in the insulation layers can increase the thermal transmittance of highly insulated envelopes by a factor of 2.2, while closed- and open-loop thermal bypass can increase it by a factor of 3.5 and 3.7 respectively. Although these might be considered extreme cases, the results align with findings from several past studies that quantified the gap between in-situ and design thermal transmittance. The results suggest that, while ensuring building envelopes are designed with adequate levels of insulation is important, ensuring that the insulation is adequately installed is potentially of equal importance. [ABSTRACT FROM AUTHOR]

• Automatic MiC envelopes and layout generation by a novel SSG-MOO method. • Multi-objective optimization formulation for passive MiC design. • A pilot MiC study in Hong Kong produced 5 selected Pareto optima out of 625. • Up to 0.42% energy savings and 9.71% spatial daylight autonomy improvement against the baseline. • A multi-level analysis of results and design strategies for practitioners. Modular-integrated Construction (MiC) is an emerging construction technique promoted in the building sector for high productivity and low waste emission in the construction phase; yet, the standardized modules also bring new challenges, such as balancing passive energy efficiency and spatial daylight autonomy, to the operational phase. This paper proposes a Symmetric Skeleton Grammar-based Multi-Objective Optimization (SSG-MOO) method to formulate parametric MiC envelopes and detailed layout, with the two objective functions being energy efficiency and interior daylight performance in the operational phase. Pareto optima of the SSG-MOO, computed by the Non-dominated Sorting Genetic Algorithm II, are generally verified and analyzed in three levels, i.e., MOO's solution space, SSG layout, and MiC design parameters. A case study of MiC residential building in Hong Kong demonstrated the SSG-MOO method through five new passive MiC designs (i.e., spatial reorganization of three architectural modules, and parameter tuning of the envelops and corridors), achieving up to 0.42% energy savings and 9.71% spatial daylight autonomy improvement compared to the baseline design. The contribution of this paper is two-fold, including a novel and sound SSG-MOO formulation for parametric MiC designs, and offering time-efficient and evidence-based design tactics for MiC designers and industrial practitioners to push boundaries of MiC. [ABSTRACT FROM AUTHOR]

• Switchable insulation is an effective means of regulating heat through envelopes. • Current research on switchable insulation is fragmented across multiple domains. • We review existing technologies systematically and propose a classification system. • We assess their performance and improvement opportunities for building applications. Switchable thermal insulation, in the form of an opaque panel that alternates between thermally conductive and insulated states, can be an effective means of regulating the thermal environment by selectively transferring heat between the indoor and outdoor environments. Pioneering work has been undertaken by researchers to develop switchable insulation technologies intended for applications in the built environment, automotive, and aerospace, where conventional space heating and cooling technologies are either too bulky or too energy consuming to meet design requirements. Switchable insulation technologies are in their infancy and the emerging research on this topic is unstructured and fragmented across disparate application sectors and very few of the adaptive insulation concepts and technologies are actively being pursued by the buildings research community. The aim of this paper is therefore to advance the understanding of switchable insulation for possible applications in the building envelope, by reviewing and classifying the existing technologies systematically, with a particular focus on their working principles, theoretical performance and improvement opportunities. The paper provides a qualitative and quantitative assessment of the most promising switchable insulation technologies for building envelopes and identifies the opportunities for further research in each of the technologies. [ABSTRACT FROM AUTHOR]

Highlights • Energy-efficient home retrofits of Slovenian house owners are studied. • Econometric models analyse partial and integral approach to retrofits. • Drivers and barriers for the building retrofit decisions are determined. • Various information sources influence energy-efficient retrofit decisions. • Free public energy audits have failed to deliver the expected outcome. Abstract The paper examines what determines household energy-efficient retrofit decisions in Slovenia based on analysis of more than 1000 single family-house owners, survey respondents. While previous studies typically stress the importance of professional energy audits, this paper shows that other formal and informal sources of information and advice also play a significant role in informing homeowners and fostering energy-efficient retrofit decisions. Compared to partial retrofit measures, an integral approach to retrofits is more strongly associated with estimated energy saving potential, household income and professional advice seeking. On the other hand, free public energy consultation appears to have failed to deliver the expected outcome. Results of econometric analysis on barriers and drivers for building energy retrofits call for strengthening the financial incentives, informing and educating the public through various information sources, combined with the redesign of public Energy Advisory Network to increase the reach of residential building energy-efficiency policies. [ABSTRACT FROM AUTHOR]

Abstract In the European Union, substantial ratio of the primary energy is consumed by the buildings, thus designing energy saving structures is mandatory in the Member States. The ratio of these energy efficient buildings, however, is evanescent compared to the vast number of existing, ineffective stock. The energetic refurbishment of the older, historical buildings raises several additional questions related to the possible methodology of their renovation. Authors of the present paper survey the architectural and geometric features of a Budapest downtown district. The area consists mainly of characteristic, ornamented, multi-storey apartment buildings. Present paper aims to answer the following question: can the energy demand be estimated by using only easily obtainable data? The significance of the above question lays in the possibility of simple, large-scale energy estimation, which can be used as a benchmark for future rehabilitation action plans. The heating energy demand and the characteristic geometry of the buildings were investigated, as well as their relationship with architectural style. The paper also introduces a structural typology for the downtown building stock of Budapest. The results show that the heating energy demand can be estimated based on geometry and architectural style data, in case of the downtown residential stock. [ABSTRACT FROM AUTHOR]

Forecast uncertainties pose a considerable challenge to the success of model predictive control (MPC) in buildings. Numerous possibilities for considering forecast uncertainties in MPCs are available, but an in-depth comparison is lacking. This paper compares two main approaches to consider uncertainties: robust and stochastic MPC. They are benchmarked against a deterministic MPC and an MPC with perfect forecast. The MPCs utilize a holistic building model to reflect modern smart homes that include photovoltaic power generation and storage, thermally controlled loads, and smart appliances. Real-world data are used to identify the thermal building model. The performance of the various controllers is investigated under three levels of uncertainty for two building models with different envelope performance. For the highly insulated building, the deterministic MPC achieves satisfactory thermal comfort when the forecast error is medium or low, but the thermal comfort is compromised for high forecast errors. In the poorly insulated building, thermal comfort is compromised at medium and high forecast errors. Compared to the deterministic MPC, the robust MPC increases the electricity cost by up to 4.5% and provides complete temperature constraint satisfaction while the stochastic MPC increases the electricity cost by less than 1% and fulfills the thermal comfort requirements. [ABSTRACT FROM AUTHOR]

Reducing energy consumption in buildings and increasing renewable production are key goals of European policies to achieve a sustainable and competitive low-carbon economy by 2020 and beyond. Non-residential buildings constitute a heterogeneity sector characterized by high energy consumption and various building types, sizes and energy characteristics over Europe. This paper presents the overall results of the data collected by the GreenBuilding Programme (GBP), launched in 2006 to promote and improve energy efficiency in new and existing European non-residential buildings. The GPB involved building owners willing to adopt energy efficiency measures to decrease energy consumption of their buildings by of at least 25%. Based on voluntary participation, hundreds of partners joined the project, which collected data from more than a thousand buildings of different age, size, use and type (such as offices, hotels, and industry). This paper provides an overview of the Programme and its main results up to its completion in 2014. The paper focuses on building characteristics, energy performance, efficiency measures and energy savings, which are globally estimated to be around 985 GWh/year. A more detailed focus is then given to office buildings, which represent the most frequent building category in the Programme. Case studies are presented to show best practices in various countries with consolidated energy efficiency policy strategies. The paper categorises the main technological measures related to envelope, appliances and systems. It shows how a wide range of technologies are becoming an integral part of buildings and how technology plays a major role in exploiting the massive potential benefits of reducing building energy consumptions. The analysis of the results generates a reliable snapshot of European non-residential building stock. [ABSTRACT FROM AUTHOR]

The growing awareness of climate change is constantly moving the attention of designers and policymakers from typical and current scenarios to future layouts. This new approach introduces a degree of uncertainty that should be accounted in the building design process. The multitude of possible scenarios suggests considering dynamic system that can adapt themselves to unpredicted operating conditions. The aim of this study is to test a new approach and a new flexibility metric analysing the behaviour of a high-performance dynamic internal curtain in current and future scenarios. The first part of the paper focuses on the optimization of the dynamic system in the current scenario; this preliminary analysis represents the traditional design approach and is considered the baseline for all the comparisons. Hence, defining a matrix of likely scenarios, this paper explores the behaviour of three different selected control strategies – always off (i.e., no curtain), fixed control, optimized control – in the different scenarios considering possible variations of i) climate, ii) urban context, iii) internal loads, and iv) building use. The main outcomes of the research are, on the one hand, the comparisons of the control strategies and the benefits of the dynamic system and, on the other hand, the definition of a new metric that can properly describe the flexibility of the building systems with reference to the future scenarios analysed. This metric is based on six classes – which describe the statistical distribution of the energy consumptions of all the scenarios simulated in the matrix – and an index called Energy Flexibility Index (EFI) that quantifies the flexibility of the technology considered. The case study analysed highlights how implementing a dynamic shading system, considering the same building use, can increase the EFI of nearly 21% for non-optimized control strategy and up to 23% when optimized. While changing the building use in residential reduces these values respectively, to 6% and 7%. [ABSTRACT FROM AUTHOR]

The building sector of the U.S. currently consumes over 40% of the U.S. primary energy supply. Estimates suggest that between 5% and 30% of any building's annual energy consumption is unknowingly wasted due to pathologically malfunctioning lighting and comfort conditioning systems. This paper presents analytical methods embodied within useful software tools to quickly identify and evaluate selected faults in air handling units (AHU) that cause large building energy inefficiencies. Algorithm for faults like stuck dampers and leaking dampers have been developed and tested in this particular case study. These damper fault detection algorithms can be applied to outdoor air and return air dampers both. The technical contributions of this work include expert rules that adapt to the Heating, Ventilation and Air-Conditioning (HVAC) equipment scale and operation and methods for sorting fault signals according to user-defined interests such as annual cost of energy inefficiencies. The combination of expert-rule based fault detection combined with first principles thermodynamic modeling is a unique contribution of this work that leads to quicker fault detection with minimal non-intrusive measurements. These contributions are particularly unique in their treatment of models and the careful consideration of user-interests in fault evaluation. As a first step to developing this general framework for fault detection, first-order faults such as stuck dampers and imbalanced airflows within several large air-handling units were targeted. The algorithms focused on detecting faults with minimal data and non-intrusive measurements. An example is presented of the potential energy savings in a large academic building that has been monitored. Real data from an academic building in Boston was collected from the building energy management system (BEMS) for the purpose of this study. Savings of around $3400/month or 18% of the monthly cost when a stuck damper fault occurred over an entire month in an air handler. Another fault that was investigated was that of leaking dampers which lead to savings of approximately $500/month or around 2.5% of the monthly energy expense. The savings would accrue if the fault were corrected; otherwise the occurrence of the fault causes a waste of money and energy predicted. The algorithms developed can be applied to large commercial office buildings, academic buildings, hospitals and other modern buildings in various climates as long as they have data collecting capabilities. [ABSTRACT FROM AUTHOR]

[Display omitted] • The paper offers an overview of the implementation of key building energy efficiency requirements in Member States. • The renovation rate has to further increase to achieve zero-emission buildings. • The EU building stock has the potential to achieve a remarkable 94% reduction in direct greenhouse gas emissions by 2050. • Member States strongly rely on renewable technologies deployment for achieving climate neutrality. • Average energy performance level of Nearly-Zero Energy Buildings (NZEB) lags behind EU benchmarks. The European Union (EU) is accelerating its clean energy transition in order to reach climate neutrality in 2050 through targeted policies, with the Energy Performance of Buildings Directive (EPBD) playing a pivotal role in reducing buildings energy consumption. This study evaluates the progress of EU Member States in implementing the EPBD requirements, focusing on Long-Term Renovation Strategies (LTRS), Nearly-Zero Energy Buildings (NZEB), Energy Performance Certificates (EPC), and technical building system inspections. Harmonized data enable cross-country comparisons, revealing trends across climatic zones and assessing policy impacts at the EU level. Aligning renovations with 2020 LTRS could cut greenhouse gas emissions by 35% by 2030 and 94% by 2050. Despite the well-established status of NZEB, their energy performance lags behind EU benchmarks in all climatic zones. Moreover, diverse EPC methodologies complicate in-depth comparisons, yet nearly 70% of EU certification schemes define energy classes based on an energy indicator. Finally, over 75% of Member States implemented heating and cooling system inspection schemes, but few assess their impacts. While progress towards a highly energy-efficient building stock is evident, challenges persist. Member States must intensify efforts, especially in increasing the renovation rate with the aim of achieving zero-emission buildings. The recast EPBD, nearing adoption, will assist Member States by imposing enhanced requirements for building decarbonisation. This effort holds potential to unlock co-benefits, including climate change mitigation, energy resilience, and improved life quality. [ABSTRACT FROM AUTHOR]

Cogeneration (CHP) and Heat Pump (HP) are playing a key role in energy systems due to their high efficiency, especially, in energy refurbishment of buildings and industrial processes. This paper explored the opportunity to couple those two well-established technologies for heating purposes. Basically, the coupling entails limitations and constraints consisting of the mismatch between the power sizes of merchandised machines as well as the deriving technical issues. The main aim of this paper is to analyse the coupling procedure to help HVAC specialists in their job. This paper deals with a simple model formulation, based on the First Law of thermodynamics, for CHP/HP systems rating and off-design operations. Indeed, an analytical method was designed along with the definition of a size factor. Then, a graphical method for coupling CHP and HP was presented so as to be an expeditious tool in designing phase. Finally, two numerical applications were illustrated highlighting the energy performance gains and the high level of operation flexibility. [ABSTRACT FROM AUTHOR]

Energy efficiency is one of the main pillars of the European energy strategy and it involves all the economic sectors. The present study proposes an analysis of the energy saving opportunities available in the Italian hotel sector, which has a relevant importance in the national context. The paper introduces a comprehensive model of the Italian hotel sector developed in the LEAP (Long-range Energy Alternatives Planning) environment. The first aim is to estimate the baseline of the consumption, then to evaluate the maximum potential energy saving and, finally, to assess the implementation of a realistic energy efficiency scenario. The paper demonstrates that it may be possible to achieve primary energy savings of 1.6 TWh (13%) in 2030 by implementing financially sustainable energy efficiency measures. [ABSTRACT FROM AUTHOR]

The present paper illustrates the Italian pilot action of the EASEE project (Envelope Approach to improve Sustainability and Energy Efficiency in existing multi-storey multi owner residential buildings), focusing on the analysis of energy refurbishment trends of the Lombardy region residential building stock. More than 60% of the existing building stock in Lombardy region has been built before the 70 s and it is among the main responsible causes of final energy absorption and corresponding CO 2 emissions with a mean primary energy index for heating of about 202.0 kWh/m 2 y. The promotion of renovation measures for such buildings, also through innovative solutions, is becoming increasingly important for both containing the greenhouse gas emissions and supporting the growth of the construction sector. To this topic, the work presents the energy renovation of a residential building in the Province of Milan through the application of innovative prefabricated composite panels that integrate both thermal insulation and exterior finishing with textile reinforced mortar. In detail, the paper describes, on one hand, the main design strategies following the preliminary analysis and, on the other hand, the whole installation process; besides, the performance of the retrofitted envelope is documented by the results of the monitoring campaign. [ABSTRACT FROM AUTHOR]

Energy efficiency standards for household appliances have been adopted in many countries to mitigate negative externalities, reduce emissions, and save energy. Using Chinese provincial-level panel data from 2008 to 2018, this paper builds a difference-in-difference model to examine the mechanism by which energy-efficiency upgrades in China's air conditioners affect urban household electricity usage. The results show that the upgrades have brought about an obvious reverse effect, with urban household electricity usage increasing rather than decreasing. Reducing the unit costs of energy service has enhanced residents' sensitivity to cooling demand. Driven by the substitution and income effects, urban residents may increase the intensity and frequency of their AC use. Additionally, this reverse effect is heterogeneously dependent on the family size and urban residents' age structure rather than their educational level. When families are larger and have more children, the reverse effect is more pronounced. This paper's findings provide useful recommendations for policy-makers to formulate policies that are synergistic with energy efficiency standards for household appliances. [ABSTRACT FROM AUTHOR]

Feature engineering and data-driven classification models are at the forefront of analysis of large temporal sensor data from the built environment. In previous efforts, temporal features were engineered from the whole building hourly electrical meter data from 507 non-residential buildings. These features fall within the three general categories of statistics, model, and pattern-based and can be used to identify various behavior in the structure of the whole building electrical meter data. In this paper, a deeper investigation is made of exactly what types of behavior are most important in the context of two classification scenarios: the primary use of a building and the level of performance the building has when compared to its peers. The highly comparative time-series analysis (hctsa) toolkit is used to analyze the most important temporal features for the classification of various building performance attributes. In the first analysis, a comparison is made to distinguish the behavior between university dormitories (70 buildings) and laboratories (95 buildings) as an example of interpreting the classification of the primary-use-type of a building. In the second analysis, a comparison of buildings with high (165 buildings) versus low (169 buildings) consumption is used to extract and understand the behavior that indicates the level of the energy performance of a building. These two case study examples provide a foundation for further explainable machine learning techniques in both classification and prediction as applied to buildings. This effort is the first example of machine learning with an explicit focus on the interpretability of classification for smart meter data from non-residential buildings. [ABSTRACT FROM AUTHOR]

• Development of a new model was developed to estimate the technical potential for net zero energy buildings. • It considers different locations, climates and building types and vintages. • It combines methods for bottom-up energy modeling and geospatial analysis. • Results show a significant energy savings in all regions and building types. With the Paris Agreement coming into force, global efforts will need to maximize opportunities through energy efficiency and renewable energy generation. Zero energy/carbon initiatives are mushrooming worldwide, but it has not been fully understood which building types in which climates and under which conditions can potentially be built to net zero energy standards. In order to inform these efforts, a new model was developed to estimate the technical potential for building—integrated solar energy (BISE, the name of the model) generation in a high resolution regional, climate and building typology breakdown., The BISE model also evaluates the opportunities for potential net zero energy buildings based on the BISE findigns, combining these with the findings of two global low-energy building models. The BISE model has a very high resolution in terms of geographic regions, climate types, building types and vintages. Moreover, the model combines methods for bottom-up energy modeling and geospatial analysis. The thermal building energy demand estimation is based on the 3CSEP-HEB model and the plug load scenarios are based on the BUENAS model. Results are wide, due to intrinsic limitationso of the model detailed in the paper, but it is shown that there is a substantial potential for building-integrated solar energy generation in all world regions, and that the Deep Efficiency Scenario allows significantly more building types to meet net zero energy levels by 2050 in contrast to a scenario when only moderate energy efficiency improvements are implemented. [ABSTRACT FROM AUTHOR]

The massive collection of data via emerging technologies like the Internet of Things (IoT) requires finding optimal ways to reduce the created features that have a potential impact on the information that can be extracted through the machine learning process. The mining of knowledge related to a concept is done on the basis of the features of data. The process of finding the best combination of features is called feature selection. In this paper we deal with multivariate time-dependent series of data points for energy forecasting in smart buildings. We propose a methodology to transform the time-dependent database into a structure that standard machine learning algorithms can process, and then, apply different types of feature selection methods for regression tasks. We used Weka for the tasks of database transformation, feature selection, regression, statistical test and forecasting. The proposed methodology improves MAE by 59.97% and RMSE by 40.75%, evaluated on training data, and it improves MAE by 42.28% and RMSE by 36.62%, evaluated on test data, on average for 1-step-ahead, 2-step-ahead and 3-step-ahead when compared to not applying any feature selection methodology. [ABSTRACT FROM AUTHOR]

• Examination of a typical classroom in Greece and its installed power for an artificial lighting profile from 1970 until today (type and number of luminaires, installed power per classroom, consumed annual energy kWh/m2). • Calculation of potential energy savings for customizing installed power LED with AC and DC supply aiming for the illuminance target values from the European Norm 12464–1, 2011. • Measurements of dimming curves of LED luminaires with AC and DC supply (consumed energy versus lighting output) and measurements of power of typical photosensors. • Estimated energy savings, CO 2 reduction and cost optimal analysis due to various scenarios (different luminaire technologies and daylight harvesting techniques). • Calculation of the final energy consumption for potential power supply from renewable energy sources using DC LED luminaires and photosensors. The energy consumption for the artificial lighting in school buildings is one of the main consumers of electricity. In Greece, there is a large number of school buildings with quite old lighting systems using luminaires with T8 and sometimes T12 fluorescent lamps and degradated translucent diffusers. Due to the economic crisis, the public sector failed to invest in LEDs and although daylight is adequate during the year, the adoption of control technology is rather slow. The paper, using a typical classroom of a Greek public school, examines a number of lighting technologies (with AC and DC supply) together with two daylight harvesting systems. The first one uses one stand-alone photosensor per luminaire while the second one sensor per control zone, in an effort to calculate energy savings and light adequacy. The dimming curves of the DC LEDs were measured along with the installed power of the photosensors. The results show that the existing 90.5 kWh p /m2 of annual lighting primary energy consumption can be reduced to 0.55 kWh p /m2. The maximum annual reduction of CO 2 emissions was 32.44 kgr/m2 for classroom areas, which is translated to 201929 tn for the whole country. It is evident that the path to Zero Energy School Buildings goes through the mandatory use of daylight controls. Image, graphical abstract [ABSTRACT FROM AUTHOR]

• VFD operating data obtained from VFD analog outputs and measured by a power analyzer are compared. • Efficiencies of the motor-belt, VFD and drive system are evaluated for three preset VFD voltage controls. • VFD-controlled analog output voltage is fundamental RMS voltage rather than total RMS voltage. • Linear ratio control has the highest drive system efficiency. Variable frequency drives (VFDs) are widely applied on induction motors to reduce electrical power by adjusting power frequency. Meanwhile VFDs also adjust power voltage based on different voltage controls, including a linear ratio, a squared ratio and a flux optimizer. Moreover, VFDs measure motor operating data, such as frequency, current, voltage and power, and provide them through their analog outputs. However, it is not clear whether the VFD analog output data are accurate and how the different voltage controls impact the drive system performance. The purpose of this paper is to investigate the accuracy of VFD analog output data and the energy performance of different voltage controls through an experiment conducted on a VFD-motor-belt-fan system. First the operating data provided by the VFD and measured by a power analyzer were compared, then the efficiencies of the motor-belt, VFD, and drive system among different voltage controls were measured and evaluated. The experiment results show that the VFD provides accurate frequency and current but inaccurate voltage and power. The squared ratio results in the highest drive system efficiency, 2–12% higher than the linear ratio and 0–2% higher than the flux optimizer, as the frequency varies from 35 to 20 Hz. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a feasibility study on the use of multilayer neural networks to determine airflow infiltration from thermographs, thus the related energy leak. The developed method, aimed at accurate evaluation of intake airflow through an opening in the building's envelope, uses as input data infrared images of the temperature changes in a rendering surface near an opening in the building's envelope. Data collection of these measurements can be achieved with relative simplicity, and therefore could lead to an alternative or complementary method to the standardised ways of measuring infiltration based on Blower Door test, increasing the possibilities of monitoring, supervision and continuous management of building's ventilation and airtightness. Laboratory results show over 93% average accuracy for instant samples, and over 98% global accuracy for sequences. The generalization capability of this method has also been explored, and several neural network topologies analysed. [ABSTRACT FROM AUTHOR]

Abstract In recent years, technological advances have substantially extended the capabilities of building automation. Despite the achieved advances, evidently, automation have not been widely adopted by occupants in buildings. To enhance automation adoptability, automation procedure in buildings should involve determination of user's preferred automation levels, in different conditions and control contexts and learning preference dynamics in time. In line with this motivation, in this paper we introduce a building automation that learns occupant's preferences continuously to fully or partially control the service systems in buildings based on a set of dynamic rules that are generated with the insight about user's preferences and activities. The algorithmic components of our proposed automation include (1) dynamic command planning, (2) adaptive local learning, and (3) iterative global learning. In order to evaluate these algorithms, we used a combination of real and synthetic user activity and preference data from an office with five occupants and an apartment with one occupant. Based on our results from evaluation of adaptive local learning, after a certain number of days (i.e., 8.5 days in average) the accuracy of predicting participants' preference reached to an acceptable value (i.e., above 85%). About 24% to 75%, 5% to 45%, and 6% to 49% of the total daily energy consumption of the participants could be saved using full automation, adaptive automation and inquisitive automation, respectively. Our results for evaluating iterative global learning algorithm showed that adaptive automation has the highest sum of the rewards from achieved benefit and user satisfaction and inquisitive automation has the second highest reward values. Full automation and no automation came in third and last spots, respectively. [ABSTRACT FROM AUTHOR]

Highlights • A novel cloud computing application for measurement and verification 2.0 is presented. • IntelliMav offers a solution for near real-time energy savings verification. • Powerful machine learning regression techniques are automatically applied to energy data. • Parallel processing using powerful cloud computing infrastructure reduces resources required. • A real-world case study demonstrates the ease of use and benefits of the application. Abstract Energy conservation measures (ECMs) are implemented in all sectors with the objective of improving the efficiency with which energy is consumed. Measurement and verification (M&V) is required to verify the performance of every ECM to ensure its successful implementation and operation. The methodologies implemented to achieve this are currently evolving to a more dynamic state, known as measurement and verification 2.0, through the use of automated and advanced analytics. The primary barrier to the adoption of M&V 2.0 practices are the tools available to practitioners. This paper aims to populate the knowledge gap in the industrial buildings sector by presenting a novel cloud computing-based application, IntelliMaV, that applies advanced machine learning techniques on large datasets to automatically verify the performance of ECMs in near real-time. Additionally, a performance deviation detection system is incorporated, ensuring persistence of savings beyond the typical period of analysis in M&V. IntelliMaV allows M&V practitioners to quantify energy savings with minimum levels of uncertainty by applying powerful analytics to data readily available in industrial facilities. The use of a cloud computing-based architecture reduces the resources required on-site and decreases the time required to train the baseline energy model through the use of parallel processing. The robust nature of the application ensures it is applicable across the broad spectrum of ECMs in the industrial buildings sector. A case study carried out in a large biomedical manufacturing facility demonstrates the ease of use of the application and the benefits realised through its adoption. The energy savings from an ECM were calculated to be 2,353,225 kWh/yr with 25.5% uncertainty at a 90% confidence interval. [ABSTRACT FROM AUTHOR]

Abstract Convenience stores are known to have higher Energy Use Intensities (EUIs) than most other commercial building types. However, convenience store energy performance analyses have not been systematically investigated. Moreover, current national convenience store benchmarks are inconsistent from one data base to another due to lack of relevant energy data. This paper assesses the energy performance of more than five hundred convenience stores in the same fleet in Eastern United States. The investigation aims to establish benchmarks for convenience stores with and without gasoline pumping stations. The main factors influencing the actual convenience store building energy use were determined. Multiple regression models were developed to find relationships between annual energy consumption per unit area and gross floor area, customer transaction count, building operating year, etc. The statistically characterized results are meant to serve as a reference data set in comparing the whole-building energy performance index for buildings of similar functionality. The statistical correlation analyses can strengthen the robust prediction of energy performance in convenience stores, enabling proper assessment of energy conservation measurements (ECMs) for convenience store operations, as well as guiding additional such energy performance studies. [ABSTRACT FROM AUTHOR]

Highlights • Non-intrusive techniques are proposed to infer occupancy from existing HVAC sensors. • Reheat energy use is reduced by more than 38% with better nighttime setback schedules. • Derived a comprehensive set of candidate anomalous zones using unsupervised learning. • Proposed approach can be readily applied to any commercial building with a BMS. Abstract The Heating Ventilation and Air Conditioning (HVAC) system of commercial buildings traditionally runs on a fixed schedule that does not take occupancy into account despite its huge variation over space and time, thereby wasting a lot of energy in conditioning empty or partially-occupied spaces. Occupancy information is essential to eliminate wasteful energy use with imperceptible impact on building operations and human comfort. This paper investigates the application of non-intrusive techniques to obtain a rough estimate of occupancy from coarse-grained measurements of sensors that are commonly available through the building management system. Various static and adaptive energy-efficient schedules are developed based on this approximate knowledge of occupancy at the level of individual zones. Our experiments in three large commercial buildings confirm that the proposed techniques can uncover the recurring occupancy pattern of the zones, and schedules that incorporate these occupancy patterns can achieve more than 38% reduction in reheat energy consumption while maintaining indoor thermal comfort. [ABSTRACT FROM AUTHOR]

Abstract With the unprecedented advancement of Internet of Things (IoT), automatic occupant activity recognition is becoming realizable for a myriad of emerging applications in smart buildings for energy efficiency and user experience enhancement. Existing activity recognition approaches require either the deployment of extra infrastructure or the cooperation of occupants to carry dedicated devices, which are expensive, intrusive and inconvenient for pervasive implementation. In this paper, we propose DeepHare, a deep learning-based h uman a ctivity re cognition scheme that can automatically identify common activities using only commodity WiFi-enabled IoT devices. We design a novel OpenWrt-based IoT platform to collect Channel State Information (CSI) measurements from commercial IoT devices. Moreover, an innovative deep learning framework, Autoencoder Long-term Recurrent Convolutional Network (AE-LRCN), is proposed. We developed dedicated neural network architectures to sanitize the noise in raw CSI data, extract high-level salient features and reveal the inherent temporal dependencies among data for accurate human activity recognition. All the parameters in AE-LRCN are fine-tuned end-to-end automatically. Extensive experiments are conducted in typical three indoor environments and the experimental results demonstrate that DeepHare outperforms existing methods and achieves a 97.6% activity recognition accuracy without human intervention. [ABSTRACT FROM AUTHOR]

Highlights • This study presents a detailed review on IRT for the investigation of building envelope defects. • The literature survey is conducted regarding existing IRT methodologies for building envelopes. • The previous studies with measurement methods, analysis schemes and analysis types are categorized in the literature matrix. • Recent advances and future trends on IRT methods are presented. Abstract Thermal radiation generated by limited resources is emitted from the surfaces and interpreted as temperature. On the other hand, the limited resources necessitate prevention of energy leakages causing noticeable temperature differences on surfaces. Providing sensitive information on temperature contrast, infrared thermography (IRT) is a prominent tool for detecting infrared energy on the surfaces of the objects. IRT is also very promising method for detecting thermal irregularities, air leakages and even moisture abnormalities on building envelopes. Since the concept of nearly zero energy buildings is to be the main target of energy policies in European Union published by European Commission, improving thermal performance of buildings becomes crucial in the last decade. Therefore, implementation of IRT for visualizing and analyzing concealed defects on building envelopes is very promising for improving the energy efficiency of buildings. Given recent regulations and benefits of emerging thermogram technologies mentioned above, a detailed review of the previous studies investigating the abnormalities of building envelopes by IRT is presented in this paper. Furthermore, the applications with measurement methods, analysis schemes and analysis types are classified to highlight the potential of IRT in inspection of building envelopes for providing energy efficient solutions. Finally, recent advances and practical future opportunities are presented for additional contribution of the IRT methods previously employed. [ABSTRACT FROM AUTHOR]

Highlights • The accuracy of current CIBSE TM benchmark for University Campus (UC) was assessed. • A new revised benchmark called UCrb was presented which is more accurate than the current benchmark. • There is a difference of 110 kWh/m2/yr between UC and UCrb which is a significant improvement. • A new methodology for thermal energy estimation at the monthly scale was developed. Abstract According to Chartered Institution of Building Services Engineers, CIBSE TM46 benchmarks, a typical college building needs 240 kWh/m2 thermal energy annually. This paper firstly analysed the accuracy of the CIBSE TM46 ‘University Campus’ benchmark against the thermal consumption values displaying on Display Energy Certificates (DECs). The DECs of 52 typical college buildings in Dublin were assessed and based on the 46% discrepancy between the median of samples and the CIBSE benchmark a new benchmark of 130 kWh/m2/yr was recommended. Secondly, monthly thermal energy models (MTEMs) which take into account the variations of heat demand during a year were generated. MTEMs were created using five key factors, including the mixed use method, CIBSE UC recommended benchmark, activities area, heating degree days, and typical operational hours of heating systems. The high level of accuracy of MTEMs was assessed against the actual consumption. The developed methodology predicts an accurate and detailed estimation of monthly thermal energy consumption in typical college buildings. In addition, a new definition for typical college buildings which is a mixed use building including seven major activities is presented. [ABSTRACT FROM AUTHOR]

Highlights • A highly interactive web tool for the exploration of NILM results is presented. • Data cube allows insightful integration of NILM components with raw consumption data. • Hospital total demand, 7 cooling machines and temperature each minute for a year. • Fluid feedback by live coordinated views allows to interpret the NILM decomposition. • NMF exploits positivity of energy consumptions for an interpretable decomposition. Abstract Non-intrusive load monitoring (NILM) techniques have recently attracted much interest, since they allow to obtain latent patterns from power demand data in buildings, revealing useful information to the expert user. Unsupervised methods are specially attractive, since they do not require labeled datasets. Particularly, non-negative matrix factorization (NMF) methods decompose a single power demand measurement over a certain time period into a set of components or “parts” that are sparse, non-negative and sum up the original measured quantity. Such components reveal hidden temporal patterns which may be difficult to interpret in complex systems such as large buildings. We suggest to integrate the knowledge of the user into the analysis in order to recognize the real events inside the electric network behind the learnt patterns. In this paper, we integrate the available domain knowledge of the user by means of a visual analytics web application in which an expert user can interact in a fluid way with the NMF outcome through visual approaches such as barcharts, heatmaps or calendars. Our approach is tested with real electric power demand data from a hospital complex, showing how the interpretation of the decomposition is improved by means of interactive data cube visualizations, in which the user can insightfully relate the NMF components to characteristic demand patterns of the hospital such as those derived from human activity, as well as to inefficient behaviors of the largest systems in the hospital. [ABSTRACT FROM AUTHOR]

Highlights • Cool roofs can improve thermal comfort of low income households near the equator. • Experimental results in Jamaica confirm improvements. • Energy models were developed for Jamaica, Ghana and Recife-Brazil. • Internal ceiling surface temperatures are reduced on average by 3.2–5.5 oC. • Cooling demand reduction is similar at 190 kWh/m2/year. Abstract Cool roofs are most effective in reducing cooling loads and alleviating overheating in locations with high solar radiation and external air temperature. This paper presents results of an experimental study of a low income house in Jamaica and a computational study in three countries around the equator: Jamaica, Northeast Brazil (Recife) and Ghana. A case-study typical of single storey houses in Jamaica was monitored before and after the installation of a cool paint on the roof; on days with average solar radiation intensity of ∼420 W/m2 and ambient air temperature of ∼28 °C, internal ceiling surface temperature is reduced by an average of 6.8 °C and internal air temperature by 2.3 °C. Monitoring results were used to calibrate successfully an EnergyPlus model; similar models were developed for Ghana and Brazil differing in size and/or construction to reflect country specific practices. Annual simulations indicate that internal ceiling surface temperatures are reduced on average by 3.2–5.5 oC and internal air temperatures by 0.75–1.2 °C. Cooling demand simulations (setpoint 24 °C) indicate similar annual potential savings in the three locations (∼190 kWh/m2/year) although estimated CO 2 emissions reduction differ reflecting electricity generation fuels. Aging of the cool roof has an impact reducing load savings by 22–26 kWh/m2/year. [ABSTRACT FROM AUTHOR]

The design of a complex power supply infrastructure and the satisfaction of the concentrated high energy demand is a constant challenge for data centre owners. Furthermore, economics of the operation and ideas of sustainability are getting more and more attention lately as well. Green data centres are designed to minimize the effect on the natural environment. One of the primary goals of such units is to decrease the energy needs both for the computing infrastructure and the supporting systems (e.g. thermal management), which can be achieved either by increasing the efficiency of processes or by integrating renewable energy sources. Present paper introduces results of an ongoing research and development cooperation between the Centre for Energy Research of the Hungarian Academy of Sciences and Persecutor Ltd., supported by the Hungarian Government’s “PIAC_13” programme. The project aims to develop a new, highly energy efficient data centre infrastructure, which is able to supply IT needs at a high-level, while minimizing power losses. Present paper focuses on decreasing power losses in the distribution and power conversion infrastructure. A power loss model, capable of supporting the optimal selection of different subsystems of the technological chain, is introduced and two case studies are shown to demonstrate the effect of different loss reduction options. [ABSTRACT FROM AUTHOR]

As buildings become more energy efficient and automated, the role of occupants becomes more significant to assist buildings in reaching their full energy efficiency potential. Predicting occupants’ consumption behavior has been identified as one of the most challenging processes in energy efficiency programs and building management system operations. Thus, more accurate behavior learning algorithms for commercial buildings are needed to improve energy efficiency. In this paper, we propose a method to predict occupants’ energy-use behavior based on individual energy consumption profiles and assess its potential to increase the effectiveness of energy efficiency programs. The proposed method implements a support vector machine in order to model and predict occupants’ short term energy-use patterns and test hypotheses for the existence of a correlation between occupants’ entropy, efficiency and prediction accuracy. The results show an average accuracy of 83% for individual energy-use pattern prediction while being positively correlated with individuals’ energy-use behavior. The main contributions of this paper are: (1) proposing and validating a new method to predict individuals’ energy-use patterns based on their individual workstation-level energy consumption patterns, and (2) assessing the feasibility and potential of implementing this method to enhance the efficacy of energy efficiency programs to further induce energy efficiency. [ABSTRACT FROM AUTHOR]

Designing a low energy building requires to evaluate a large number of parameters: design combinations, element thermal characteristics, HVAC systems efficiencies, etc. All of them determine the space heating and space cooling energy demands, energy consumptions, and life cycle cost over the lifetime of the building. This paper presents the achievements of SEDICAE research project consisting on a new methodology based on a tabu search and a simplified method to calculate energy consumptions. The methodology is designed to estimate the annual energy demand, life-cycle cost and energy rating. A second process is included to select the HVAC system is developed for the building designed with this methodology. In general, builders should look for low energy, but investment in building design is a limiting factor. This paper show the relationship between energy efficiency index and investment in optimized residential building. Furthermore, the best energy rate is obtained applying the HVAC systems in the integrated optimization method for residential buildings. This methodology reduces investment, reduce the energy demand and selects the best construction materials, allowing to study the cost in different economic scenarios. In this paper, a building design case study is studied in 2028 scenarios in 12 climatic zone in Spain. [ABSTRACT FROM AUTHOR]

This paper aims to improve our knowledge of energy use and well-being in energy efficient office buildings. It explores the interrelations between forms and patterns of energy use on the one hand and user satisfaction, comfort, and well-being on the other hand. Findings are derived from qualitative and quantitative data collected in a recently finished research project in Austria. Fieldwork consisted of two qualitative case studies as well as an online-based survey amongst users of energy efficient office buildings. In addition, secondary data from energy-monitoring research was used to underpin the analysis. The results show that extremely low levels of energy use in office buildings can be aligned with high levels of well-being. Based on empirical findings the paper argues that both well-being and energy performance in office buildings are the result of a complex, on-going and intertwined process that involves various material and social elements. [ABSTRACT FROM AUTHOR]

Many post-occupancy comfort studies of energy renovated residential buildings have documented elevated temperatures above comfort levels, not only during the summer period but also during the shoulder months. The main focus in renovation projects is on heat savings while the risk of overheating is not considered. This paper analyze in which situations overheating and cooling need to be addressed in building energy renovation projects and which renovation measures are causing this need. The analysis contains four reference single family houses from central and northern Europe. Both dynamic and static methods were used to assess the overheating risk. In terms of overheating occurrences, the most critical renovation measures are the insulation of the floor and the increase of the airtightness. The contribution of decreasing the g value of the window glazing is positive. The way to energy efficiency improvements also results in an extension of the overheating period and higher average and maximum building temperatures. The increase of the ventilation rates and the use of shading systems are useful measures for preventing overheating increase. The paper will highlight the inconsistencies which arise from the use of different criteria and also propose suggestions for future work. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present a case of an office building and show how different concepts of envelope construction affect the energy efficiency of building. Assessment of energy performances of different scenarios of the hypothetical models of the office building in the central zone of Belgrade is the aim of this research. A few scenarios are created for each hypothetical model: basic scenario and scenarios of two shading devices types in order contribution to reduction of cooling demands to be estimated. The office building scenarios are modelled and analysed by using PHPP ’ 2007 software to estimate heat and cooling energy demands. Also, CO 2 emissions are discussed. Comparative analysis of the results highlights the advantages and disadvantages of the treated models and scenarios of office building envelope construction. Findings indicate the preferable solutions and highlight methodological approach in the office building envelope design, which has not yet been adopted extensively in practice in our country. Methodological approach and findings can generally be applicable for new office building design, both in Belgrade and in the similar climatic conditions. The paper demonstrates various feasible solutions of office building envelope construction that might help comply with the regulation. [ABSTRACT FROM AUTHOR]

Nowadays, aviation industry has been faced with technology challenges and safety requirements. Therefore, aircraft manufacturers give special attention to environmental building capacities regarding to passenger comfort and safety. This paper aims measuring safety and comfortability level of passenger accommodation during the flight. Since the environmental and safety issues could affect humans on a different level significance, this paper is based on the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method as a decision making tool for balancing those two inputs. With the aim of defining rank of existing world market aircraft the Saaty scale was used for developing the weight of different criteria whilst the improved TOPSIS method is used for ranking six aircraft: A320, A330, A340, B737, B747, B767 based on cabin environmental efficiency and safety. [ABSTRACT FROM AUTHOR]

This paper presents the application of multi-objective genetic algorithms for holistic building design that considers multiple criteria; building energy use, capital cost, daylight distribution and thermal indoor environment. The optimization focus is related to building envelope parameters. To obtain relevant feedback from multi-objective optimizations in early design stages, evaluation speed is a key concern. The paper presents a fast evaluation method fit for the early design stages. It uses a combination of two different quasi-steady-state methods for energy and indoor environment evaluations, a Radiance implementation for daylight simulations and a scripted algorithm for capital cost evaluations. The application of the method is developed around an integrated dynamic model which allows visual design feedback from all evaluations to be an integrated part of the design tool experience. It is concluded, that quasi-steady-state methods implemented as part of integrated dynamic models are fast and flexible enough to support building energy-, indoor environment- and cost-optimization the early design stages. [ABSTRACT FROM AUTHOR]

• Five different energy efficiency alternatives for the building were studied. • A ground-source heat pump and solar PV usually provide the highest profitability. • A household battery has become profitable at the 2022 electricity price. • Dimensioning of a solar PV system massively depends on the electricity price level. • At the 2022 electricity price level, the array-to-inverter ratio can be up to 4.9. Buildings and the construction sector make up about a third of the final energy consumption and energy-related carbon dioxide emissions, offering a significant potential for emission reductions. This paper investigates the conversion of a Nordic oil-heated townhouse into a carbon-neutral one. Five different energy efficiency (EE) alternatives including, for example, different heat pump types and their dimensioning are studied. The electricity demand of the EE alternatives is aimed to be covered as cost-effectively as possible with an on-grid system based on solar photovoltaics (PV) and a battery energy storage system (BESS). The cost-optimal dimensioning of the components is based on the 30-year life cycle cost (LCC), and the work is carried out using an hourly level simulation model developed in MATLAB. To maximize the profitability of the BESS, an intelligent control algorithm based on linear programming is implemented based on a day-ahead electricity spot price for the years 2020–2022 and "perfectly" forecasted power demand and generation data. The data used in the simulation consist of measured electricity consumption, simulated heat generation based on annual oil consumption, and simulated solar PV power generation. It was observed that an about 60% partial-power-dimensioned ground source heat pump together with a solar PV system provides almost always the lowest LCC. However, the BESS was shown to be profitable at the current investment costs for the first time at the 2022 electricity prices, enabling a capacity of 210–230 kWh. On average, the cost-optimized on-grid electricity system was found to reduce the 30-year life cycle costs by 6%, 20%, and 85% with the electricity prices of 2020, 2021, and 2022, respectively. When the price of electricity is exceptionally high, it may be justified to implement more extensive energy efficiency improvements, such as additional insulation of the building envelope. Nevertheless, the impact on the life cycle costs is marginal. In addition, the cost-optimized solar PV capacity has increased more than sevenfold between 2020 and 2022. [ABSTRACT FROM AUTHOR]

The low energy efficiency of the European building stock is both a social and environmental issue. However, plans and studies on energy retrofitting interventions do not usually evaluate the impact of these measures on households' energy affordability. This paper analyses and compares the effect of alternative retrofitting strategies on thermal energy services' affordability in Spanish vulnerable households. This analysis was carried out by applying the Required Thermal Energy Expenditure model proposed in previous studies to several 2030 residential building stock scenarios, setting 2020 as a baseline. Therefore, this work proposes a methodological approach to assess the impact of the considered energy efficiency interventions on energy poverty in winter (WEP) and summer (SEP) and carries out both a 'social cost' and a cost-benefit analysis. The dwelling's thermal-enclosure retrofitting stands out as a very WEP-reduction and social-cost effective measure. Instead, replacing old thermal systems with more efficient ones would have a higher impact on SEP. Combining both kinds of retrofitting measures could reduce heating and cooling required expenditures by, respectively, 49% and 59%, and decrease the WEP and SEP share, correspondingly, by 35% and 63%. These results might inform policymakers to enhance the targeting and design of energy efficiency programs. [ABSTRACT FROM AUTHOR]

• The new solution for the façade integrated photovoltaic system, En-ActivETICS was proposed and described in detail, including technical recommendations. • The calibrated numerical model was used to determine hygrothermal performance and identify the most crucial issues of En-ActivETICS. • The positive effect of PCM and diffusion channels was revealed based on a numerical study using calibrated thermoelectrical and hygrothermal models. • The En-ActivETICS was investigated considering local climatic and economic conditions among 38 European countries. • The roadmap 2030 for the En-ActivETICS advancement was developed and presented. The main goal of the European building market transformation regards the reduction of CO 2 emissions and energy demand in the built environment towards net zero carbon buildings. This drives research and building practice to search for economically justified solutions that contribute to achieving these objectives. The work presented in this paper is concerned with the technological and economic context. The original solution of photovoltaic integration with the external thermal insulation composite system named the En-ActivETICS was developed, tested and analysed. The energy potential of the En-ActivETICS across Europe, considering south oriented façade is from 88.3 to 195.8 kWh/(m2⋅a), with the efficiency of solar energy conversion of around 13–14%. The problem of system overheating was solved by applying PCM mortar beside the PV panel, which allowed to keep the PV temperature below 70 °C. The risk of moisture load (from driven rain or water vapour condensation) was avoided by diffusion channels pressed in the mortar. Furthermore, the economic analysis of system implementation in Europe is also promising. Considering the availability of solar energy and electricity prices around Europe the payback time of the En-ActivETICS varies between 12 and 44 years for analysed countries. According to the presented roadmap for the En-ActivETICS advancement a payback period of <10 years is possible with a maximum cost of the system <275 EUR/m2 and electricity price >0.25 EUR/kWh. [ABSTRACT FROM AUTHOR]

[Display omitted] • Impacts of reverse cycle air-conditioners (RCACs) during summer are examined. • Wellbeing impacts are studied via a survey of 283 Australian public housing tenants. • Positive and significant impacts on measures of health and housing suitability are found. • Households with RCACs increased summertime electricity expenditure by about A$40. • There is no significant impact on subjective reporting of energy bill stress. Heating and cooling upgrades can have important implications for energy bills and thermal comfort in public housing. However, more could be known about the specific impacts of such upgrades, including on tenant wellbeing. This paper examines upgrades delivered to public housing properties in the Australian Capital Territory, focusing on effects in summer. We analyze impacts of replacing existing heaters with energy-efficient reverse cycle air-conditioners (RCACs). The principal research question is: by how much does access to indoor cooling increase wellbeing among public housing tenants? The analysis is based on a survey of 283 residents carried out in March–April 2021. We use factor analysis followed by linear regressions and an instrumental variable approach. Positive and significant impacts are found on measures of health and housing suitability, including the reported ability of residents to concentrate. Households with RCACs increased their summertime electricity bills by around A$40 relative to control households, although we find no significant impact on subjective reporting of energy bill stress. The study is among the first to apply rigorous econometric techniques to examine causal impacts of indoor cooling in residential settings. [ABSTRACT FROM AUTHOR]

Providing the occupants with tailored feedback on their energy consumptions can be an effective method to help the users to adopt energy-efficient behaviours. However, in specific contexts such as the social housing, this may be challenging, due to the peculiarities and the economic constraints of the end-users, which make them vulnerable and needing a proper design of the information. Nevertheless, there is a lack of detailed studies in the literature analysing the effects of personalized energy feedback on the space heating consumption and the indoor air temperatures in the social housing sector. This paper aims to evaluate the effects on space heating consumption and indoor air temperature of a personalized feedback campaign delivered in three social housing buildings consisting of 27 apartments in Mediterranean climate. To this end, a personalized feedback campaign was designed and developed, providing for the use of disaggregated energy consumption indicators to show the location of excessive thermal energy consumptions. The daily thermal energy consumptions, as well as the indoor air temperatures of the single dwellings, were monitored daily for over four heating seasons and their variations were analysed before and after the implementation of the personalized feedback. A saving in normalized natural gas consumption of the buildings was found on average equal to −14,8% after the feedback campaign, with no significant variations in the indoor air temperatures. However, some critical issues have been highlighted in the response of individual end-users to the feedback provided. A greater propensity of the occupants to regulate the indoor air temperature through the thermostatic radiator valves in the unoccupied rooms of the house was also observed after the occurrence of the feedback campaign. [ABSTRACT FROM AUTHOR]

Energy consumption of lift systems is one key issue in modern high-rise buildings, drawing property owners' attention and accounting for 6% of total electricity consumption by end-use in Hong Kong. A Benchmarking Energy Performance Indicator (BEPI), , measured in J.kg−1.m−1 and proposed by the first author of this paper for lift installations is mentioned in the Technical Guidelines of the Building Energy Code , as one emerging good engineering practice. BEPI is a fair parameter for comparing energy performance of lifts irrespective of their brand, model, travel, speed, capacity and control. It considers energy performance due to both the motor drive efficiency and traffic control intelligence. However, one main obstacle to make it statutory is the lacking of a generic monitor able to retrieve three sets of real time data (normally unavailable on an existing lift) to compute the BEPI, namely energy consumption, instantaneous car load and traveling distance, and to collect ample data statistically reliable enough to determine an acceptable threshold value. This paper describes the development of a proof-of-concept prototype BEPI monitor by giving full technical details on the rationale and history of development, the hardware components, software framework, and updated numerical results of one implementation inside the headquarters of Electrical & Mechanical Services Department (EMSD) of HKSAR. Experiences in installing and calibrating those external sensors that make the whole system non-disturbing to normal lift operation, as well as the procedures in computing the BEPI, are discussed in greater details. It has been shown that the BEPI is traffic dependent but the weekly average of it is relatively stable, confirming it may be robust enough for benchmarking purpose. The solution described in this paper may help roll out the monitoring of BEPI for all lifts inside and outside Hong Kong. [ABSTRACT FROM AUTHOR]