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• 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]

Post-occupancy evaluation (POE) is necessary to evaluate the building's performance in order to formulate future design development after its utilisation. This paper evaluates indoor thermal performance, sound pressure level, and illuminance in a multipurpose auditorium by combining zoning and activity-based POE. The evaluation was done in three activities: (A) student graduation, (B) theater performance, and (C) public lecture with different types of clothing preferences. Three different zones were used to define the precise location of the building improvement. Field measurements were taken for thermal performance, including Operative Temperature (T op), Humidity (Rh), Sound Pressure Level (dB), and Illuminance (lux). User perceptions were collected from 171 respondents (89 men and 82 women). The field measurement showed that average operative temperature varied across the activities with 26.15–25.68 °C, 25.47–24.95 °C, and 23.74–23.17 °C on activities A, B, and C, respectively. Activity A's (1 clo) room's operative temperature showed that 65% of data are outside the ASHRAE standard, indicating that the room condition is unsuitable for 1 clo. Activity A's TSV shows a slightly warm sensation (0.21 to 0.93) and is still in the comfort range. With the same clothing value (0.6 clo), Activity B's T op indicates a more suitable condition than Activity C's. The average TSV in Activity B indicates slightly cool (-0.54 to −0.90), with the lowest TSV found in Activity C (-1.09 to −1.41). Although TSV positively correlates with PMV , Thermal Preference (TP) revealed that the occupant expects cooler conditions in all activities. Sound pressure level and illuminance performed well according to all activities. This paper recommends chiller setting adjustment in the multipurpose auditorium based on activity characteristics. Combined POE based on zoning and activity can indicate which aspects of the auditorium need to be improved. [ABSTRACT FROM AUTHOR]

Several studies have demonstrated the capability of data-driven modelling based on on-site measurements to characterise the thermal performance of building envelopes. Currently, such methods include steady-state and dynamic heating experiments and have mainly been applied to scale models and unoccupied test buildings. Nonetheless, it is proposed to upscale these concepts to characterise the thermal performance of in-use buildings based on on-board monitoring (OBM) devices which gather long-term operational data (e.g., room temperatures, gas and electricity consumption...). It remains, however, to be proven whether in-use data could be a cost-effective, practical and reliable alternative for the dedicated tests whose more intrusive measurements require on-site inspections. Furthermore, it is presently unclear what the optimal experimental design of the OBM would be and which data analysis methods would be adequate. This paper presents a first step in bridging this knowledge gap, by using on-board monitoring data to characterise the overall heat loss coefficient (HLC) [W/K] of an occupied, well-insulated single-family house in the UK. With the aid of a detailed building physical framework and specifically selected data subsets a sensitivity analysis is carried out to analyse the impact of the measurement set-up, the duration of the measurement campaign and the applied data analysis method. Although the exact HLC of the building is unknown and no absolute errors could hence be calculated, this paper provides a new understanding of the decisions that have to be made during the process from design of experiment to data analysis. It is demonstrated that such judgements can lead to differences in the mean HLC estimate of up to 89.5%. [ABSTRACT FROM AUTHOR]

• Affordable and adaptable VIP based internal and external retrofitting solutions. • Implementation of VIP based integral retrofitting solutions in two tertiary buildings. • Space heating energy saving analysis before and after VIP based retrofit. • Envelope heat loss coefficient reduction analysis before and after retrofitting. Vacuum insulation panels (VIPs) represent a huge opportunity to achieve nearly zero energy buildings, especially in the retrofitting of existing buildings. VIPs can be applied in conjunction with façade cladding elements, considerably reducing the heat losses or gains from the building by means of very slim wall construction systems or retrofit solutions. However, the fact that VIPs cannot be cut on-site, can be easily damaged during their handling, installation and use and have an envelope that can cause thermal bridging in the junction between panels or when combined with other materials, has hindered their penetration in the construction market. This paper presents two innovative façade retrofitting solutions, a rear ventilated façade and a stud mounted internal solution, that have tackled all these issues and provide thermal transmittances of 0.28 and 0.14 W/m2 K in relatively slim insulation systems of 10 cm widths. Solutions have been installed on the entire façade of two existing buildings located in Bilbao (Spain) and Malmö (Sweden) subjected to an integral rehabilitation process, covering 2100 m2. Buildings energy use have been monitored before and after the renovation, showing energy cuts of around 35% from pre-intervention status. This paper presents the lessons learnt during the design and installation process of the vacuum insulation integrating solutions in the façade of two existing buildings and the thermal performance results achieved after their retrofitting. [ABSTRACT FROM AUTHOR]

Highlights • To our knowledge this is the first journal paper to include in-depth thematic analysis of expert interviews regarding the use of building performance simulation in Australia, with a particular focus on lower quality office buildings. • In this paper, the typical simulation and upgrade process operating in practice in Australia is compared with a previously outlined optimal strategy, and a 'Revised Systematic Approach' to sustainable building retrofits is proposed. • The thematic analysis of interview responses identified many of the drivers and barriers influencing the user of building simulation tools. • Several practices employed by practitioners that are likely to greatly increase the uncertainty associated with simulation results were identified. • The lack of reliable and accurate data was highlighted as a key issue facing this sector, and the implications of the uncertainty introduced by the necessary use of assumptions, particularly concerning innovative financing initiatives, is discussed. Abstract Building Performance Simulation (BPS) has become an important tool in the upgrading of the existing commercial building stock. It allows a user to predict energy savings from upgrades, and thereby optimise the energy, financial or other benefits of an energy efficiency upgrade. The current paper presents the findings from a qualitative investigation into the use of BPS in the Australian commercial building retrofitting industry. The interviews had a particular focus on challenges for lower quality (known as secondary grade or mid-tier) office buildings. Twelve semi-structured interviews were undertaken with stakeholders in the building energy retrofitting industry, including consultants, government administrators and building managers. Thematic analysis of the interview data revealed numerous challenges for BPS users, primarily related to the lack of reliable, accessible data regarding building operations and energy use in Australia. The poor data availability encouraged the use of simulation to inform decisions but required the modeller to use assumptions and heuristics to develop the model. This, in turn, necessitated a reliance on the expert knowledge of the simulation user, and made the interrogation of a building model by a third party problematic. [ABSTRACT FROM AUTHOR]

This paper analyzes electricity use in buildings and explores key research questions related to assessing, rating, labeling, and benchmarking building energy use. The analysis reviews annual Energy Use Intensity (EUI) data from 3060 fully electric-powered buildings across six categories: residential, educational, offices, hospitals, hotels and restaurants, and wholesale and retail trade services. One-fifth of the analyzed buildings is derived from an energy dataset that offers a higher time-scale resolution, providing more detailed insights into energy usage patterns. The analysis employs various factors, including the seasonal variation factor (SVF), daily variation factor (DVF), monthly factor (M f), and hourly factor (H f), to gain a detailed understanding of actual energy usage at different time scales. The findings indicate that electricity use is less sensitive to seasonal variations than other energy carriers that provide heating and cooling. The paper provides hourly and monthly peak load percentages (PPL), which can indirectly assist in assessing hourly schedules for building energy performance simulations. The monthly energy use pattern varies between building categories, with high-energy months alternating between winter and summer. For non-residential buildings, high-energy hours correspond directly to the daytime, while for residential buildings, there is a shift towards the evening, and the daily amplitude is lower. Furthermore, the analysis of daily and monthly variability of heating energy carriers, even if non-electric, sheds light on the implications of electrification for these loads. • The study evaluates annual electricity data from 3060 fully electric-powered buildings. • Median EUIs are meaningful energy performance benchmarks by building category. • Residential and educational buildings exhibit EUIs of 90-120 kW h/m2. • Offices and hospitals demonstrate EUIs of 160-170 kW h/m2. • Monthly and hourly factors establish energy benchmarks for finer time resolution. [ABSTRACT FROM AUTHOR]

Building performance simulation is a powerful tool applied both in research and for building design including heating, ventilation, and air-conditioning systems. Several research studies have used this tool to investigate the potential of energy savings measures for swimming facilities. However, in these facilities the technical complexity is a considerable challenge in modelling since many complex phenomena occur in the various sub-systems. The building industry traditionally plans and design these complex buildings by heuristic rules of thumb, and these empirical design rules may lead to significant differences between real and predicted performance. Therefore, it's important to develop simplified models with acceptable accuracy for simulation-based design of swimming facilities. The scientific literature introduces different simplifications for the air handling unit modelling, but the impact of these simplifications is hardly analyzed. Therefore, the paper investigates the model complexity of the air handling unit in a Norwegian swimming facility by comparing a novel simplified model and a detailed model which serves as a digital replica of a real unit. The simplified decoupled model is found to have an acceptable accuracy for early-stage design, bearing in mind the overall uncertainty at this design stage. However, when carrying out a detailed investigation of a swimming facility, the detailed model should be applied, since this complex heuristic rule-based model, is taking into account phenomena like thermal-coupling effects between the swimming pool and the air handling unit and the control errors of the real internal controller. In conclusion, the paper derives useful guidelines for building performance simulation design of air handling unit, which is the main energy-intensive component in indoor swimming pool facilities. [ABSTRACT FROM AUTHOR]

[Display omitted] • Discusses the concept of building performance simulation versus new digital trends. • Reviews Artificial Intelligence, Machine Learning, Digital Twins, Cyber-Physical Systems, Internet of Things, Data Mining. • Explores common notions about differences, similarities, strengths and limitations of each concept. • Reviews emerging work on the interface between these concepts. • New concepts enjoy traction and energize the building performance research area. In an increasingly digital world, there are fast-paced developments in fields such as Artificial Intelligence, Machine Learning, Data Mining, Digital Twins, Cyber-Physical Systems and the Internet of Things. This paper reviews and discusses how these new emerging areas relate to the traditional domain of building performance simulation. It explores the boundaries between building simulation and these other fields in order to identify conceptual differences and similarities, strengths and limitations of each of these areas. The paper critiques common notions about these new domains and how they relate to building simulation, reviewing how the field of building performance may evolve and benefit from the new developments. [ABSTRACT FROM AUTHOR]

• Overheating regulations and calculation methods in 26 European countries were compared. • Most of the existing calculation methods are outdated and do not fit climate-proof buildings. • France requires a mixed-mode operation of naturally ventilated households. • The UK developed a heatwave-based calculation approach. • Comfort-based, multi-zonal, and time-integrated calculation approaches are needed. With the ongoing significance of overheating calculations in the residential building sector, building codes such as the European Energy Performance of Building Directive (EPBD) are essential for harmonizing the indicators and performance thresholds. This paper investigates Europe's overheating calculation methods, indicators, and thresholds and evaluates their ability to address climate change and heat events. e study aims to identify the suitability of existing overheating calculation methods and propose recommendations for the EPBD. The study results provide a cross-sectional overview of twenty-six European countries. The most influential overheating calculation criteria are listed the best approaches are ranked. The paper provides a thorough comparative assessment and recommendations to align current calculations with climate-sensitive metrics. The results suggest a framework and key performance indicators that are comfort-based, multi-zonal, and time-integrated to calculate overheating and modify the EU's next building energy efficiency regulations. The results can help policymakers and building professionals to develop the next overheating calculation framework and approach for the future development of climate-proof and resilient residential buildings. [ABSTRACT FROM AUTHOR]

[Display omitted] • A split-degree days index is proposed for building energy use/climate classification. • Split-degree days index is shown to better estimate solar radiation and humidity data. • The estimation of building energy end-uses is improved using the split-degree days. • Split-degree days also showed improved results for high thermal mass and 24-hr models. • Split-degree days index maintains a high accuracy for varying base temperatures. Degree days have been widely used in different applications in buildings, such as estimating building energy use and climate classification for building energy standards. However, there are limitations with the use of conventional degree days that result in inaccuracies in estimating building energy consumption using degree day-based methods. This paper proposes a new method, the split-degree day method, that shows substantially improved results in the accuracy of the building energy use estimation compared to the conventional degree day methods. The analysis in 801 locations in the U.S. using regression models showed that the new, proposed split-degree day method in this paper, compared to the conventional degree day method, better accounts for the weather parameters and more accurately estimates end-uses. The split-degree days showed an improvement of over 5% in the accuracy of the total annual energy use prediction, 8% for predicting the heating energy use, 0.3% for predicting the cooling energy use, and 33% for predicting the fan energy use. Additionally, the analysis showed improved results for the model with higher thermal mass and the model with a 24-hour operating schedule. These improvements were due to the fact that the split-degree day method includes more information related to the weather characteristics of a location, which is missing in a conventional degree day calculation procedure that relies on aggregated data at daily intervals. Furthermore, as opposed to the conventional degree days, the accuracy of the estimations using the split-degree days are shown to maintain their high accuracy when the base temperature deviates from the optimal base temperature. The proposed method can be used as a more accurate alternative to the conventional degree days in different applications, including but not limited to building climate classification, building energy estimation, and weather normalization for building energy savings calculation. [ABSTRACT FROM AUTHOR]

Commercial buildings during operation are dynamic environments where changes to control strategies and space usage regularly occur. As a result of these and other issues, a performance gap between design intent and actual building performance emerges. This paper seeks to address the operational performance gap and enhance operational building performance through statistically-based fault detection. Additionally, this paper seeks to remedy the knowledge gap building managers face in the identification of key building faults based on minimal quantities and streams of time-series building data. A new methodology is presented that incorporates simulation and breakout detection to address these issues. Residual based exponentially weighted moving average (EWMA) charts and Shewhart charts are compared against a breakout detection algorithm to identify shifts or faults in building performance data. Artificial faults are introduced into the measured time-series data to test the validity of the chosen statistical techniques. Statistical metric sensitivity and precision are calculated to quantify the performance of the new methodology. A summary of results demonstrate that the breakout detection algorithm was the most effective method in detecting meaningful faults in building performance data, followed by residual based EWMA and Shewhart models. [ABSTRACT FROM AUTHOR]

The need for improved building performance is global and has led to numerous international collaborations. Since 2000, the International Energy Agency (IEA) Energy in Buildings and Communities Program (EBC, formerly ECBCS ) has completed two building commissioning research annexes, recognizing the role of the building commissioning process in meeting energy conservation goals. Annex 40 established a common understanding of the commissioning process and initiated research that became the basis for the development of national guidelines and automated tools. Subsequently, Annex 47 focused on advancing the use of technology in commissioning and the work developed and ideas exchanged in Annex 47 were foundational in enabling numerous positive impacts internationally. This paper describes research results from these annexes 1 1 Certain commercial entities, equipment, or materials may be identified in this document in order to describe an experimental procedure or concept adequately. Such identification is not intended to imply recommendation or endorsement by the National Institute of Standards and Technology, nor is it intended to imply that the entities, materials, or equipment are necessarily the best available for the purpose. and discusses the impact of both in four areas: 1) commissioning support software tools, 2) cost-benefit methodology, 3) guidelines and standards, and 4) industry associations and research conferences. The paper concludes with a discussion of current commissioning industry research and policy needs. [ABSTRACT FROM AUTHOR]

While IEA EBC Annexes usually are focused on R&D about single energy technologies, their broad scale use in practice is of even higher importance to contribute to global energy goals. This practical application of low-carbon technologies on community scale has been the subject of Annex 51, carried out from 2010 to 2013. In this Annex, evaluating 25 community energy case studies of the 10 participating countries, a ‘Guidebook on Successful Urban Energy Planning’ has been extracted from these evaluations to directly address practical urban/energy planners and municipal decision makers. The main content of the Guidebook with respect to planning approaches is summarized in this paper, distinguishing between the different planning fields of ‘urban energy planning’, which is addressing whole cities and communities, and ‘local energy planning’, oriented to neighbourhoods or building clusters. The necessary link between both is emphasized, considering a city as an interconnected energy system requiring a holistic approach rather than a compartmented consideration. As a side effect, a general method to calculate the primary energy performance of buildings and neighbourhoods employing different energy systems, including energy efficiency or energy conservation technologies, was derived. Using appropriate system characteristics, such as seasonal performances, this approach can be applied for every energy system, and for every building or neighbourhood. It is presented in this paper supplying an example from one of the case studies. [ABSTRACT FROM AUTHOR]

Model predictive control (MPC) for heating, ventilation and air conditioning (HVAC) in buildings requires accurate controller models of the building envelope and its HVAC systems. Controller models are typically obtained by means of black- or grey-box system identification or using a white-box modelling approach. However, the necessary level of model complexity used by each method in order to obtain good MPC performance remains a priori unknown and no systematic method or examples showing the optimal complexity is available. This paper systematically investigates the required controller model complexity necessary to obtain optimal control performance for a given building. First, a 6-room house is modeled in detail using building energy simulation software. The building model is then linearized to obtain a linear time invariant (LTI) state-space model (SSM) and the upper bound of the control performance is computed using an MPC with the SSM both as controller and as plant model. The accuracy of the SSM (containing more than 250 states) is then artificially decreased by reducing its number of states to different orders ranging from 4 to 100 using balanced truncation model order reduction technique. The performances of MPCs using these controller models are then compared with the upper bound for both a standard MPC formulation (S-MPC) and an offset-free formulation (OSF-MPC) and with the performance of a rule-based-controller (RBC). The procedure is repeated for the same house model with a higher level of insulation and for a lighter weight construction. This paper shows that the controller model should contain a minimum of states to model each zone separately, and that the walls and floors separating the zones should also have enough states to act as a low pass filter with correct cut-off frequency. The minimum number of states further increases with the building mass content. In the case of the investigated 6-room house, the thermal comfort achieved by MPC using a controller model with a minimum of 30 states instead of 20 states was improved with a factor 2–6 without significant increase of the energy use, showing that good MPC performances require controller models with a significantly higher number of states than the order used by most of the black- and grey-box system identification techniques. The minimum required number of states might be chosen lower when OSF-MPC is used instead of conventional MPC. However, OSF-MPC might significantly increase the energy use when poor controller models (high model mismatch) are used. Furthermore, if the controller model is an LTI model, this paper shows that the CPU time necessary to solve the MPC optimization problem becomes independent of the number of states of the controller model when a dense approach is used. The controller model can thus be as complex as necessary to produce accurate predictions without increasing the computation time of the optimization. [ABSTRACT FROM AUTHOR]

[Display omitted] • 33 worldwide open datasets of city-level building energy consumption are summarized. • Representative applications of 198 papers using the collected datasets are analyzed and compared. • Policy implications are presented for using the open datasets to support policymaking. • Useful non-energy datasets about weather, building features, urban forms, and occupant-related factors are recommended. • Solutions to privacy issues are explored, including anonymizing methods and proper data aggregation. Data related to building energy use fuels the research and applications on building energy efficiency, which is an essential measure to address global energy and environmental challenges. However, in most cities, there is a lack of comprehensive and publicly accessible building energy use datasets with necessary temporal and spatial granularities to support urban building energy modeling, regional energy planning, city-level building performance benchmarking, and policymaking on building energy efficiency. Data owners and governments are facing challenges in determining which and how building energy use data should be disclosed at the city level and how to protect data privacy. This review paper provides insights to answer these questions based on a comprehensive and critical review of worldwide open datasets and their applications in the built environment context. Detailed information about the collected 33 building energy datasets is summarized and categorized. Studies identified into 11 subdomains using these open datasets are critically analyzed and compared. Potential policy implications based on the studies are also proposed. Moreover, non-energy datasets that are frequently used in research relevant to urban building energy use are also introduced. Solutions to privacy issues are discussed to address concerns from data publishers. Finally, significant conclusions are made to support the proper disclosure of city-level building energy data. This review study is valuable to urban building energy data disclosure, urban building energy modeling, and data-driven energy policymaking. [ABSTRACT FROM AUTHOR]

Reducing space heating energy demand supports the UK’s legislated carbon emission reduction targets and requires the effective characterisation of the UK’s existing housing stock to facilitate retrofitting decision-making. Approximately 6.6 million UK dwellings pre-date 1919 and are predominantly of suspended timber ground floor construction, the thermal performance of which has not been extensively investigated. This paper examines suspended timber ground floor heat-flow by presenting high resolution in-situ heat-flux measurements undertaken in a case study house at 15 point locations on the floor. The results highlight significant variability in observed heat-flow: point U-values range from 0.56 ± 0.05 to 1.18 ± 0.11 Wm −2 K −1 . This highlights that observing only a few measurements is unlikely to be representative of the whole floor heat-flow and the extrapolation from such point values to whole floor U-value estimates could lead to its over- or under- estimation. Floor U-value models appear to underestimate the actual measured floor U-value in this case study. This paper highlights the care with which in-situ heat-flux measuring must be undertaken to enable comparison with models, literature and between studies and the findings support the unique, high-resolution in-situ monitoring methodology used in this study for further research in this area. [ABSTRACT FROM AUTHOR]

[Display omitted] • Building policies must be rooted in a good understanding of the targeted population. • Building performance standards may benefit from creating databases of weather files. • A thermal performance analysis should consider multiple key performance indicators. • Building performance policies should address the thermal resilience of buildings. This paper presents the main challenges faced and lessons learned when developing a new method to assess the thermal performance of residential buildings for the recently updated Brazilian building performance standard, NBR 15575–1:2021. It touches on subjects related to the representation of occupant behavior, climate, and thermal acceptability in building performance simulation, as well as the use of reference models, key performance indicators, and performance levels in policies. NBR 15575 provides a thorough procedure to analyze thermal performance, allowing buildings to be assessed while in passive operation mode and also accounting for energy needs when active operation is necessary. Multiple key performance indicators are introduced to provide a comprehensive evaluation. The main difficulty found was dealing with diversity. Given the vast scale of Brazil's territory, a high variation in culture, climate and construction techniques is expected, thus requiring a compromise between detail and scalability. There are still many opportunities for improvement, especially regarding the representation of occupants, and adjustments of the characteristics of reference models to promote high thermal performance, considering the diverse climates, regional practices, and economic needs. This paper might help researchers and other stakeholders to develop and improve other local standards and protocols, especially for warm climates. [ABSTRACT FROM AUTHOR]

• A data-informed building energy management (DiBEM) framework was proposed. • It was demonstrated in an office/laboratory building with two-years' experimental data. • The impacts of energy efficient interventions were quantified through data-driven models. • The energy use intensity was reduced from 54.1 kWh/m2 to 42.8 kWh/m2. To support building operations in reaching ultra-low energy targets, this paper proposes a data-informed building energy management (DiBEM) framework to improve energy efficiency systematically and continuously at the operation stage. Specifically, it has two key features including data-informed energy-saving potential identification and data-driven model-based energy savings evaluation. The paper demonstrates the proposed DiBEM with a detailed case study of an office and living laboratory building located in Cambridge, Massachusetts called HouseZero. It focuses on revealing the performance of the energy-efficient interventions from two-years' building performance monitoring data, as well as evaluating energy savings from the interventions based on the data-driven approach. With Year 1 as baseline, several interventions are proposed for Year 2 including improvements to controls and operation settings, encouragement of occupants' behavior for energy savings, and hardware retrofitting. These were deployed to heating/cooling, domestic hot water, lighting, plug and other loads, and photovoltaic (PV) systems. To quantify the impacts of different interventions on energy end uses, several data-driven models are developed. These models utilize linear regression, condition model, and machine learning techniques. Consequently, the heating/cooling energy consumption that was already ultra-low in Year 1 (12.8 kWh/m2) is further reduced to 9.7 kWh/m2 in Year 2, while the indoor thermal environment is well maintained. The domestic hot water energy is reduced from 2.3 kWh/m2 to 1.2 kWh/m2. The lighting energy is only increased from 0.3 kWh/m2 in pandemic operations without occupancy in Year 1 to 0.8 kWh/m2 in partial normal operations in Year 2, while the indoor illuminance level meets occupants' requirements. Combined with other relatively constant loads and the reduction of plug and other loads due to COVID building operation restrictions, the total energy use intensity is thereby reduced from 54.1 kWh/m2 to 42.8 kWh/m2, where 5.4 kWh/m2 of energy reduction for Year 2 is estimated to be contributed by the energy-efficient interventions. PV generation is 36.1 kWh/m2, with an increase of 1.4 kWh/m2 from a new inverter. In summary, this paper demonstrates the use of DiBEM through a detailed case study and long-term monitoring data as evidence to achieve ultra-low energy operations. [ABSTRACT FROM AUTHOR]

Numerous buildings fall short of expectations regarding occupant satisfaction, sustainability, or energy efficiency. In this paper, the performance of buildings in terms of occupant comfort is evaluated using a probabilistic model based on Bayesian networks (BNs). The BN model is founded on an in-depth analysis of satisfaction survey responses and a thorough study of building performance parameters. This study also presents a user-friendly visualization compatible with BIM to simplify data collecting in two case studies from Norway with data from 2019 to 2022. This paper proposes a novel Digital Twin approach for incorporating building information modeling (BIM) with real-time sensor data, occupants' feedback, a probabilistic model of occupants' comfort, and HVAC faults detection and prediction that may affect occupants' comfort. New methods for using BIM as a visualization platform, as well as a predictive maintenance method to detect and anticipate problems in the HVAC system, are also presented. These methods will help decision-makers improve the occupants' comfort conditions in buildings. However, due to the intricate interaction between numerous equipment and the absence of data integration among FM systems, CMMS, BMS, and BIM data are integrated in this paper into a framework utilizing ontology graphs to generalize the Digital Twin framework so it can be applied to many buildings. The results of this study can aid decision-makers in the facility management sector by offering insight into the aspects that influence occupant comfort, speeding up the process of identifying equipment malfunctions, and pointing toward possible solutions. [ABSTRACT FROM AUTHOR]

Research has shown the large effect that occupants have on buildings' performance. Uncertainties related to the actual energy consumption of buildings increase the risks for the investments in low carbon technologies. Monitoring building occupancy can potentially decrease these uncertainties by providing more information about the occupants and their behaviour. The objective of the investigation is to establish an approach to inform the design process (e.g. building simulation) by addressing the complexity of occupants behaviour. The approach integrates information on occupants' behaviour and attitudes regarding energy use and indoor conditions to determine the requirements for building simulation and energy calculations. This paper presents the results of two monitoring campaigns in which the approach was employed. The monitoring campaigns focused on two owner-inhabited apartments in Spain and three social rental dwellings in The Netherlands. The results have given first insights of the power of the methodology to obtain detailed and understandable data on the occupancy patterns. This investigation highlighted the importance socio-economical status and attitudes towards energy conservation on occupants' behaviour in residential buildings. The methods described in this paper can be readily used to develop occupancy and heating profiles for monitored households to be used in building simulation programs. [ABSTRACT FROM AUTHOR]

Occupants account for a significant impact on building performance through their interactions with zone level building components. Empirically-derived occupant models, despite their potential to represent occupants’ impact in building performance simulation (BPS), have minimal penetration in design and code compliance processes. Instead, occupants are represented with static schedules or simple deterministic triggers. The objective of this paper is to better understand the influence of assumptions made in representing occupant interactions with building components over a BPS model's energy use and comfort predictions, as well as their ability to promote better design decisions. To this end, the energy and daylight performance of a generic perimeter office space in Ottawa, Canada were evaluated using a set of comprehensive performance metrics. Results indicate that representing dynamic occupant–building interactions lead to different energy predictions from the static schedules. The maximum difference in the total electricity use was for window-to-wall area ratios (WWR) of 20%, which was about 30% higher with the stochastic cases than the blind-open static cases. WWR 60% and 40% generally yielded the lowest lighting electricity use with the static and stochastic cases, respectively. This paper emphasizes the importance of incorporating empirically-derived dynamic occupant models for simulation-aided design and code compliance. [ABSTRACT FROM AUTHOR]

In a hybrid cooling system, an indirect evaporative cooling (IEC) unit is installed before an air handling unit to pre-cool the incoming fresh air, in order to reduce the energy consumption of the air-conditioning system. When the system is applied in humid regions, as the dew point temperature of the fresh air is higher, condensation will probably occur in the dry channels of IEC unit. However, the model of IEC unit considering condensation had been seldom studied, which would become an obstacle in predicting the performance of the hybrid system. In order to evaluate its energy saving potential precisely, the paper aims to establish a short-cut model for predicting the annual performance of the hybrid cooling system considering condensation in IEC unit. This paper firstly presents the analytical models for IEC under non-condensation, totally condensation and partially condensation states and proposed a method for judging the three states as well. The models were validated by the published data from literatures. The validated model was then incorporated with TRNSYS for predicting the performance of an IEC hybrid cooling system in a wet market in Hong Kong and the annual simulation results were validated by a field measurement with good agreement. [ABSTRACT FROM AUTHOR]

The increase in global environmental concerns as well as the advancement of computational tools and methods have had significant impacts on the way in which buildings are being designed. Building professionals are increasingly expected to improve energy performance of their design. To achieve a high level of energy performance, multidisciplinary simulation-based optimization can be utilized to help designers in exploring more design alternatives and making informed decisions. Because of the high complexity in setting up a building model for multi-objective design optimization, there is a great demand of utilizing and integrating the advanced modeling and simulation technologies, including BIM, parametric modeling, cloud-based simulation, and optimization algorithms, as well as a new user interface that facilitates the setup of building parameters (decision variables) and performance fitness functions (design objectives) for automatically generating, evaluating, and optimizing multiple design options. This paper presents an integrated framework for building information modeling (BIM)-based performance optimization, BPOpt. This framework enables designers to explore design alternatives using an open-source, visual programming user interface on the top of a widely used BIM platform, to generate models of building design options, assess the environmental performance of the models through cloud-based simulation, and search for the most appropriate design alternatives. This paper details the process of the development of BPOpt and also provides a case study to show its application. The case study demonstrates the use of BPOpt in minimizing the energy consumption while maximizing the appropriate daylighting level for a residential building. Finally, strengths, limitations, current adoption by academia and industry, and future improvements of BPOpt for high-performance building design are discussed. [ABSTRACT FROM AUTHOR]

• Novel 24-h perturbation method for the estimation of the Heat Transfer Coefficient. • Binary signals with 5 + -hour heating and free-floating periods show highest accuracy. • BIC model selection among RC models most accurate for physical interpretation. • Proposed method very accurate in winter conditions. • Mid-season conditions might need slighlty longer measurement durations. On-site measurement of the actual thermal performance of a building envelope could considerably benefit to the building sector by securing an effective performance of newly built or retrofitted buildings. Current methods to estimate the Heat Transfer Coefficient, for example, suffer however from either excessive equipment downtime and therefore large costs, or fail to achieve a reasonable accuracy. This paper aims therefore at developing and assessing an original 24-h perturbation method for estimating a whole-building Heat Transfer Coefficient. Based on a binary heating signal, the proposed method exploits the data collected with stochastic RC models. The paper first presents a numerical comparison of 398 binary signals along with variable initial indoor temperatures to bring out common characteristics of signals that achieve highly accurate HTC estimations. An experimental campaign in a highly insulated wooden-framed house then implemented one particular signal and assessed its reproducibility under variable weather conditions. The paper shows that the proposed 24-h method indeed achieves a highly accurate estimation when performed in cold weather. In mid-season conditions, the method shows a better robustness from 30- to 36-h measurements. The results invite to further investigate other building types in variable weather conditions to validate the proposed method. [ABSTRACT FROM AUTHOR]

[Display omitted] • nZEBs in Eastern Europe lack carbon neutrality metrics and targets. • Performance requirements for nZEBs in Eastern Europe are presented. • Eastern Europe needs strategy and local governance for nZEB market penetration. • EPCs require major reform, and effective monitoring of nZEB performance is needed. • The cost-optimality calculation method must be adopted to ensure cost-effective market uptake. The European Unions' ambition for the construction sector is to be carbon neutral by 2030 for new construction. Since 2021, all new buildings in the EU should have been constructed as nearly zero-energy buildings (nZEB). However, Eastern European countries struggle to implement the 2018 Energy Performance of Building Directive recast requirements. Next to the economic challenges, equally essential factors hinder renovating the existing residential building stock and adding newly constructed high-performance buildings sourced primarily from renewable energy sources. Therefore, this study provides a cross-study to identify the barriers to nZEB implementation in ten Eastern European countries, including Bulgaria, Croatia, Czechia, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, and Slovakia. The study was conducted between 2019 and 2021 and provides an overview of prospects for nZEB in Eastern Europe. The study examines the challenges of nZEB plans faced in those countries and provides constructive recommendations. The regulations and definitions regarding nZEB energy performance, cooling and heating energy demand, thermal comfort, onsite renewables, and construction quality were analyzed. Results show that most Eastern European countries are unprepared to comply with the EPBD guidelines and cost-optimality approach. The paper ranks each country and recommends specific measures to refine the nZEB definitions. The paper provides a thorough comparative assessment and benchmarking of select EU geography that can help shift the identified gaps into opportunities for the future development of climate-neutral high-performance buildings. [ABSTRACT FROM AUTHOR]

As the scope of building design and construction increases and building systems become more integrated, the use of building energy models has become increasingly widespread in evaluating and predicting building performance. Despite the growing sophistication of building modeling tools, errors can arise from approximations that are made by a practitioner during model creation. This paper examines the process of model zoning, i.e., how the volume of a building is divided into regions where properties are assumed to be uniform. Zoning is performed during model creation to decrease model complexity. However, accuracy reduces when dissimilar regions of a building are defined by a single zone. In this paper, a systematic approach to creating zoning approximations is introduced. Utilizing the Koopman operator, the time-series output produced by a building simulation can be decomposed into spatial modes which capture the thermal behavior of a building at different time-scales. Identification of spatial structures within these modes forms a framework for the creation of simplified models of varying levels of granularity. In this paper, a detailed model is analyzed, and model accuracy is studied as coarser building representations are created using the introduced method. [ABSTRACT FROM AUTHOR]

The energy consumption necessary to maintain thermal comfort in buildings remains a significant and partially unjustified part, accounting for about 30–40% of the total energy consumption. Despite the increasingly pressing regulations in the energy sector (EPBD recast), the quality of the internal environment remains one of the critical parameters on which to pay more attention in view of a consistent evaluation of the Nzeb building. The windowed components are often designed with thermal insulation performances higher than the minimum requirements but still they lack in design of the incident solar radiation control, which is a particularly delicate topic for indoor comfort and more generally for a higher energy efficiency in buildings. If we think of the numerous variables of influence (typology, orientation, geographical location, etc.), the possibilities to associate energy saving, lighting control and users' comfort are inevitably entrusted to dynamic systems integrated into the glass. In this paper authors present the results related to the experimental characterization of two identical test rooms with different typologies of windows. One room is furnished with a traditional high thermal performance glass and the other, with an electrochromic glass suitably controlled by remote. The operative temperature measured in both test rooms have been utilized to verify the simulation model developed to assess the energy saving achievable in a Mediterranean climate, taking properly into account the hourly variations of the climate and the actions on the shading regulation systems. [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]

The primary objective of this paper is to rank building faults based on their impacts on the building energy penalty and occupant thermal comfort penalty considering multiple faults and fault occurrence rates. A fault impact analysis framework is created by incorporating the fault model library with the whole building energy performance simulation (e.g., EnergyPlus used in this study). The fault occurrence rate is introduced as a "meta" parameter in the simulation. This framework involves three essential aspects of conducting a fault impact analysis: fault constructing, fault simulation, and fault impact analysis. A parametric sensitivity analysis was used to determine and rank the criticality of the faults considering the fault concurrence frequency, by using the deep-learning based response surface model (i.e., the multi-layer perceptron regression). The proposed fault analysis framework with ranking was tested and demonstrated for the DOE's prototype medium-sized office in four different climate zones (i.e., Atlanta, Chicago, Miami, and San Francisco) with 12,000 EnergyPlus fault simulations. A total of 129 fault modes from 41 groups of fault models were simulated for the medium-sized office case. The results demonstrate the proposed framework is robust and scalable for the fault impact analysis. The top critical fault for the medium-sized office is the fault of HVAC-Left-ON for the packaged rooftop unit, regarding the site energy, source energy, and HVAC energy. Excluding the fault of HVAC-Left-ON, the top critical faults vary significantly among the four climate zones. [ABSTRACT FROM AUTHOR]

• Key operational parameters of 14 office buildings were reviewed. • Over 60% of the air handling units were not turned off outside normal occupied hours. • Most of the reviewed buildings did not have an economizer cycle program. • A sensitivity analysis was conducted with the reviewed operational parameters. • A mixed-integer genetic algorithm was applied to identify the optimal operational parameters. Operator decisions regarding daily and seasonal scheduling of systems' availability and setpoints play an important role in building performance. This paper reviews the key operational parameters of 14 office buildings in Ottawa, Canada. The results revealed that over 60% of the air handling units (AHUs) were not turned off outside of normal occupied hours, and most of them did not have an economizer cycle. The indoor temperature setpoints were between 20 and 24 °C. Based on these insights, a building performance simulation (BPS)-based sensitivity analysis was conducted to better understand the energy and comfort performance implications of common operator decisions. Eight operational parameters were studied: AHU start and stop times, seasonal switchover to heating and cooling times, heating and cooling season temperature setpoints, and ventilation rate and mode (i.e., constant or occupancy-based). The results revealed that the AHU start and stop times and the ventilation rate are the most critical operational parameters examined in terms of affecting the energy and comfort performance of the buildings investigated. Subsequently, a mixed-integer genetic algorithm was applied to identify the optimal operational parameters among the set of eight operational parameters investigated for four different heating dominated climate zones, nine different occupancy and three different envelope scenarios. The relationships between the variables of these scenarios and the optimal operational parameters were examined. [ABSTRACT FROM AUTHOR]

Over the last decades, the reduction of the energy use in the building sector has become a topic of major investigation and policy development worldwide. Guidelines have been defined to drive governments and building construction stakeholders towards the retrofit of the existing building stock and to the construction of new high-performance buildings. However, availability of operational data is often limited, especially when it comes to high performance buildings in warm climates, although it is essential to define design approaches targeted to energy efficiency, to design smart energy grids and demand-response oriented energy programs. Buildings, such as living laboratories, may offer opportunities to implement and develop energy databases, to provide benchmarks and to study occupant behaviour under different operational conditions. The paper investigates the energy and thermal comfort performance of a residential building in the Mediterranean climate. The building, certified as Passivhaus and equipped with an advanced monitoring system, allows to test different control strategies, to study occupant behaviour and to provide real time operational data. In particular, the data analysis showed a positive energy balance on yearly basis, i.e. an energy use of 59.7 kWh/m2 net /year vs. an on-site energy generation of 76.0 kWh/m2 net /year. The energy breakdown highlighted, that energy uses related to user behaviour and comfort requests account for about 72% of the total energy use, confirming that occupant behaviour is one of the major drivers of the operational energy use (and the related services) in high performance buildings. [ABSTRACT FROM AUTHOR]

The construction industry in the United States consumes more than 50% of the global energy supply per year, suggesting that significant efforts may be needed to reduce building energy demand and its carbon footprint. During their lifespans, buildings consume embodied energy (EE) and operational energy (OE). Building professionals, therefore, conduct life cycle energy assessments (LCEA) to quantify and understand the paradox and interconnectedness between EE and OE. Traditionally, simulation-based optimization techniques were used for design space exploration to identify a building design with the fewest energy implications. However, literature shows these data-driven approaches are often error-prone, time-consuming, and computationally expensive, and they fail to provide real-time feedback to the user. Moreover, EE and OE assessment tools are disjointed and suffer from interoperability issues. These limitations restrict design space exploration, which eventually hinders the design decision-making process. Over the last few years, the increased availability of building data has made machine learning (ML) techniques a popular choice for building performance assessments. Several articles have developed prediction models to assess or optimize OE. While this work is significant, studies utilizing ML techniques for building LCEA are lacking, mainly due to the unavailability of a large-scale LCEA database. In this paper, we propose a methodology to (1) generate a simulation-based building energy dataset for different building typologies using a parametric framework, (2) utilize the synthetically generated database to develop an ML-based prediction model to predict EE and OE, and (3) test the model using a case study. The case study results show that the model achieves high prediction performance using minimal inputs available during the early design phase. The results further indicate that ML techniques can be used by building designers with no or limited LCE expertise to instantaneously estimate building LCE performance and help them select design options with minimal LCE consumption. [ABSTRACT FROM AUTHOR]

Among the various factors that affect building performance, occupant behavior (OB) has received more and more attention and has become a research hotspot in building design and operation in recent years. However, due to the remarkable randomness and diversity of OB in buildings, it is difficult to accurately describe its characteristics using a fixed schedule, which is a normal approach of most energy simulation tools. DeST, a whole building energy simulation tool offers the function of stochastic OB simulation so that the simulation results can be more in line with the actual situation. In the newly released DeST 3.0, the OB modules are developed based on the actual observed OB data and are divided into two parts: occupancy and occupant action modules. The occupancy module is based on the Markov chain, which simulates the occupant movements among the spaces inside and outside the buildings. The occupant action modules are proposed with an environment and event-driven stochastic infrastructure based on a three-parameter Weibull distribution, such as occupant shading, window, and air-conditioning (AC)/heating control behavior modules. This paper presents the OB modules development for coupled simulation in DeST 3.0 and discusses the effects of integrated simulation of occupant behavior on building energy consumption. Firstly, an overview of the current modelling efforts of OB is given, from the trend to the existing foundation. Secondly, the development of OB modules for coupled simulation in DeST 3.0 is introduced in-depth, including co-simulation of AC/heating control behavior, window behavior and shading behavior. Lastly, two case studies that realize coupled simulation using the developed modules are shown, one is the comparison between existing occupancy schedules from GB standards and the developed occupancy simulation, and the other is the comparison between the result of simulating with or without using AC/heating control behavior modules in two bedrooms of the residential building. [ABSTRACT FROM AUTHOR]

• The effect of the inaccurate load label on prediction models is analyzed. • The method of extracting load from cooling capacity is proposed. • Practical cases are used for validation. • The accuracy of ultra-short-term load prediction models is improved. Data-driven modeling achieves excellent performance in building load prediction by mining the actual load characteristic from operation data. When predicting room air-conditioning load, however, the measured data of cooling capacity cannot be used as the load label in the modeling as customary. The reason lies in the unstable automatic control of the air-conditioner, which makes the cooling capacity oscillate at the actual load. The mismatch of cooling capacity and actual load will affect prediction modeling. This paper first sets up a simulation experiment to quantitatively analyze this effect. The results indicate that the more the cooling capacity deviates from the load, the greater the error of the prediction model. To solve this problem, a load extraction method based on the Fourier decomposition is proposed, which involves transforming the actual cooling capacity data into the frequency domain and extracting the low-frequency components as load data, which is further used for modeling. The experiments show that the accuracy between extracted data and real load exceeds 90%. Additionally, the load extraction method improves the prediction accuracy by up to 17.55% compared to the model using cooling capacity data directly. Finally, a practical case demonstrates the effectiveness of the proposed load extraction method. [ABSTRACT FROM AUTHOR]

• Bottom-up archetype model for space heating demand of Swiss office building stock. • Archetypes characteristics derived from energy certificates database. • Estimation of current thermal performance and energy retrofit's potential. • Calculation of national heating demand and distribution among archetypes. • Influence of design characteristics and buildings properties on heating demand. Energy efficiency in buildings is a key objective of the European energy strategy. To address the limited knowledge of the thermal performance of the non-domestic building stocks, this paper presents a bottom-up, physics-based, archetypal model derived from a large building stock database and physical characteristics from 2170 building energy performance certificates in Switzerland. The model was validated by measured energy consumption data and national statistics. Results highlight the potential for energy retrofit: most of the stock is still heated with fossil fuels (67% of the total heated area) and 40% of the total envelope surface has a poor thermal performance associated with the period before 1980. We estimate national final energy demand for heating of the office building stock (11.3 PJ/year) and present detailed breakdown of energy by thermal loss element. Significant differences were found between archetypes related to urban settings (specific demand 50% higher in rural than in urban contexts) and age of the buildings (56% of the total demand is due to buildings constructed before 1970). The physical model highlights the impact of architectural design characteristics such as building compactness and glazed surface ratios on thermal performance. The model represents an important step for studies on optimal thermal retrofit pathways. [ABSTRACT FROM AUTHOR]

In recent years, measurement protocols for the estimation of the total aggregate building heat transfer coefficient (HTC) have provided sufficient empirical evidence to indicate that buildings often do not perform as intended. However, little research has been carried out into the associated uncertainties. Within this context, this paper reviews sources of uncertainty associated with co-heating tests; characterises these uncertainties and their impact on HTC estimates; and devises a method for the calculation of HTC uncertainty. The method proposed was applied to 14 co-heating tests, showing estimated total uncertainty ranging between 2.2-21.1 ▪ (or 4.6-26.7% of the measured value) with a mean of 10.1 ▪ (or 8.7%). The natural variation of HTC and often-observed inaccuracy of design calculations (the 'prediction gap') suggest that more accurate measurements may be of little benefit. Additionally, results suggest that weather conditions, challenging building design and poor experimental technique can all significantly contribute to HTC uncertainty. However, when suitable buildings are tested by experienced technicians and under suitable weather conditions, HTC estimates from the co-heating protocol are likely to provide a useful tool to assess and understand real-world building fabric performance. • Uncertainties in steady state co-heating measurements are reviewed. • Uncertainties are characterised by their nature and impact upon HTC estimates. • A framework for estimating uncertainty in HTC estimates if given. • Range of estimated uncertainty on 14 tests is 5-27%. • Cases with poor experimental control and problematic test buildings result in higher uncertainty. [ABSTRACT FROM AUTHOR]

An excellent way to obtain a detailed description of the air flows and temperature field in a zone is to perform accurate Computational Fluid Dynamics (CFD) simulations. Unfortunately, this is often prohibitive due to the powerful resources that are required, and the large computational times. As a consequence, several intermediate models between CFD and the standard well-mixed models have been proposed over the years. These are known as zonal models, and they aim at delivering sufficiently accurate predictions of the thermal comfort in a room, for long periods (months or even years), and with substantially shorter computational times. However, none of these models has yet been implemented in a building performance simulation (BPS) tool. In this work, we have developed and implemented a BLOCK zonal model [1,2] within the IDA ICE (BPS) software. A multi-node 1D model for the prediction of vertical temperature gradients is used, together with flow element theory and an appropriate algorithm for the estimation of view-factors in non-convex geometries with obstacles. The paper presents the theoretical background of the model along with advice on its range of applicability. Finally, the model is validated against several test cases, showing its suitability in predicting the thermal stratification. [ABSTRACT FROM AUTHOR]

[Display omitted] • The application of OB models in the building design process is reviewed. • The role of OB models for a robust and resilient built environment is highlighted. • The gap between the occupant behaviour research field and the end-users is mapped. • A simulation framework for integrating OB models in the design practice is proposed. • Criteria for evaluating, choosing, and adapting OB models are given. Occupant behaviour is an important source of uncertainty in building energy performance simulations. This has led to the development and integration of different modelling approaches that represent the complex, stochastic nature of human-building interaction. Yet, several barriers prevent their wide use in simulation-aided building design. The procedures and practical solutions for integrating occupant behaviour models are segmented through the literature. Accordingly, this paper examines the state-of-the-art in the application of occupant behaviour models. Based on the PRISMA methodology, the literature is critically analysed to: i) identify and map the barriers between theory and application; ii) propose a simulation framework establishing the steps for integrating occupant behaviour models into building performance simulations; iii) synthetise practical solutions and highlight remaining challenges towards a simulation framework adequately integrating occupant behaviour. The paper stresses the added value within the decision-making process at different building design stages. Furthermore, key elements for identifying the appropriated modelling approach for each occupant behaviour aspect are presented considering factors such as type of behaviour, building type, and spatial and temporal scale. Ultimately, this critical review establishes guidelines for the integration of occupant behaviour into building design practice and defines a research pathway for bridging the gap between the OB research field and the simulation-aided building design practice. [ABSTRACT FROM AUTHOR]

Abstract: Accurate understanding of the thermal behaviour of building components is essential for predicting heating or cooling needs and facilitates the implementation of more successful energy saving strategies and retrofits. This paper focuses on a specific measure commonly introduced through the residential energy efficiency retrofit programmes in Ireland–insulation. Traditionally, assessments of the performance of building envelopes have been based on assumed thermal resistances of the materials involved, laboratory tests and computer modelling. The aim of the present work is to investigate the in situ thermal behaviour of a case study building and its components under transient and quasi-steady environmental conditions, comparing data before and after the fixing of cavity wall and ceiling insulation. The paper concludes by proposing that predicted values of heat loss using standardised assumed material properties of the existing structure do not reflect the actual values achieved in situ for this test case. These values greatly overestimated the impact of the retrofitted insulation on heat loss through the ceiling and wall. [Copyright &y& Elsevier]

Abstract: Directive 2010/31/EU adopted that by the end of 2020 all new buildings should be nearly Zero-Energy Buildings (nZEB) and Member States should achieve cost-optimal levels by ensuring minimum energy performance requirements for buildings. This paper discusses how low should be the energy required by a nZEB, in the context of housing energy consumption in a Mediterranean climate (Lisbon). For selected houses built after 1990, the calculated primary energy loads for regulated uses – heating, cooling and domestic hot water – are found to be below 90kWh/(m2 year). Applying the cost-optimal solutions of thermal insulation and glazing type and considering energy efficiency improved systems, this study concludes that housing energy loads are ‘low’ for the indicative range of 70kWh/(m2 year) for regulated uses or 100–110kWh/(m2 year) for total uses, taking primary energy indicators (PEI) from EN 15603. Assuming PEI from Passive House Planning Package or those to be assumed in Portugal for 2013, the threshold decreases to 60kWh/(m2 year) for regulated uses or 90–100kWh/(m2 year) for total uses. Only the first nZEB condition is explored by this paper. The second condition requires that the nZEB energy load is covered by a ‘significant’ part of renewable energy sources produced on-site or ‘nearby’. [Copyright &y& Elsevier]

Abstract: This paper summarizes a study undertaken to reveal potential challenges and opportunities for integrating optimization tools in net zero energy buildings (NZEB) design. The paper reviews current trends in simulation-based building performance optimization (BPO) and outlines major criteria for optimization tools selection and evaluation. This is based on analyzing user''s needs for tools capabilities and requirement specifications. The review is carried out by means of a literature review of 165 publications and interviews with 28 optimization experts. The findings are based on an inter-group comparison between experts. The aim is to assess the gaps and needs for integrating BPO tools in NZEB design. The findings indicate a breakthrough in using evolutionary algorithms in solving highly constrained envelope, HVAC and renewable optimization problems. Simple genetic algorithm solved many design and operation problems and allowed measuring the improvement in the optimality of a solution against a base case. Evolutionary algorithms are also easily adapted to enable them to solve a particular optimization problem more effectively. However, existing limitations including model uncertainty, computation time, difficulty of use and steep learning curve. Some future directions anticipated or needed for improvement of current tools are presented. [Copyright &y& Elsevier]

Abstract: This paper reports the use of an internationally recognized validation and diagnostics procedure to test the fidelity of a simplified calculation method. The case study is the simplified model for calculation of energy performance of building envelopes, introduced by the Brazilian regulation for energy efficiency in commercial buildings. The first step of the assessment consisted on evaluating the simplified model results using the BESTEST. This paper presents a straightforward approach to apply the BESTEST in other climates than the original one (Denver, USA). The second step of the assessment consisted on applying the simplified model to evaluate four building typologies, and compare the results with those obtained using a state of the art building energy simulation (BES) program. For some BESTEST cases, the simplified model presented results inside of a confidence interval calculated by the authors. However, the simplified model was found to yield significant difference in the four building typologies analysed. Moreover, in all four building typologies analysed, the simplified model led to a lower energy efficiency label when compared to the label obtained using BES. The paper concludes that the simplified model may require improvements to properly indicate the actual energy performance of commercial building envelopes. [Copyright &y& Elsevier]

• Measured thermal resistance of thermal paint and general wall coverings are alike. • Thermal paint coatings & additives are thin (<1 mm) and do not show low-e surfaces. • Regular EPS liners give more effective thermal insulation levels at a lower cost. • Microscopy of insulating additives suggest the structure is not nano-porous. • The payback period is longer than a human life span for thermal paint coatings. A purported approach to reducing heating energy in solid wall "hard to heat" housing is the simple application of a thin layer (<1 mm) of thermal paint containing insulating additives. The objective of this study was to test the energy saving claims by a systematic study of the material characteristics and thermal performance of internal coatings using accepted international standard test methods. The coatings have been compared with conventional internal coverings such as emulsion paint, wallpapers and expanded polystyrene liner. A dynamic model of the Energy House research facility has been used to evaluate energy savings, costs, and payback times. The thermal resistance of the thermal paint coatings was generally found to be not much better than that of conventional vinyl textured wallpapers with a lining paper. When all building heat losses are considered, modelling predictions for thermal paint coatings indicate an unfavourable payback period of several hundred years, and energy savings of between 0.4% and 2.9% depending on coating thickness and type. The evidence from the results and models, as well as scanning electron microscopy, do not support the claims that the additive powder particles are effectively nano-porous, evacuated, or that the coatings have low emissivity surfaces. [ABSTRACT FROM AUTHOR]

• The effects of green roofs and practical green façades within courtyard area on public buildings investigated for "Hot Summer, cold Winter" region of China. • Internal surface temperatures for areas with and without external greening monitored and compared. • Heating and cooling energy consumption before and after greening compared from computer simulation as well as from smart meter readings. • Actual heating and cooling energy consumption before and after greening compared using statistical analyses. T -statistics and p -values show significant reductions were achieved. The "Hot Summer Cold Winter" climate region of China is one of the most challenging regions for providing occupant comfort in buildings, with high demands for heating as well as cooling, together with high humidity levels. Although the performance of buildings has improved with the implementation of the new Chinese National Building Standards for the Zhejiang Province, it is believed that adding passive strategies could help to alleviate the problem even further. This paper aims to investigate the effects of adding horizontal greening (green roof) and vertical greening in the courtyard area to existing public buildings in the "Hot Summer Cold Winter" climate region of China. Using computer simulation and measurements from a monitored case in the city of Ningbo, the effects of green retrofitting on human comfort and energy consumption are analysed. Simulation results suggest that with partial horizontal and vertical greening, the cooling load could be reduced by 8.8% and heating load by 1.85%. Comparisons between actual energy use before and after greening on the case building showed significant reductions in both winter and summer, confirming the positive effects of vertical and horizontal greening of public buildings in this region. [ABSTRACT FROM AUTHOR]

Data-driven models based on thermal resistor-capacitor networks (RC models) are useful in enhancing the energy performance of buildings. This paper presents a simple yet effective methodology to obtain reliable RC models for thermal dynamics of houses. In this methodology, complex preliminary model structures are first created based on physical principles and then simplified by progressively removing non-identifiable parameters to obtain the most suitable model structures. Two important techniques are adopted in the simplification process: (1) a genetic algorithm is employed during model training to ensure satisfactory fitting ability of large model structures; (2) asymptotic confidence intervals are calculated for parameter estimates and used to define parameter non-identifiability. The methodology is illustrated using a case study of a low energy house. The case study shows that the uncertainty of the obtained model is low, and the model parameters can be easily physically interpreted. Furthermore, validations of the simplified model structure show that there is no evident decrease in the fitting accuracy and the obtained model is able to characterize temperature differences between adjacent thermal zones. [ABSTRACT FROM AUTHOR]

This paper presents the development of a self-tuned HVAC controller that provides customized thermal conditions to satisfy occupant preferences (i.e., online learning) while minimizing energy consumption, and the implementation of this controller in a real occupied office space. The evolution of personalized thermal preference models and the delivery of thermal conditions with model predictive control (MPC) form a closed-loop. To integrate these two parts, we propose a new method that always provides a set of lower and upper indoor temperature bounds. Different from ad hoc rules proposed in previous research, the control bounds are based on a decision-making method that minimizes the expected cost. We implemented the self-tuned controller in an actual open-plan office space conditioned with a radiant floor cooling system with eight independently controlled loops. Localized operative temperature bounds in each radiant floor loop were determined based on occupants' feedback and personalized thermal preference models, developed using a Bayesian clustering and online classification algorithm. The self-tuned controller can decrease occupant dissatisfaction compared to a baseline MPC controller, tuned based on general comfort bounds. To generalize the findings of this work: (i) we integrated the self-tuned controller with local MPC into a building simulation platform using synthetic occupant profiles, and (ii) demonstrated a method for automatic system adjustment based on comfort-energy trade-off tuning. In this way, decisions resulting in energy waste or occupant dissatisfaction are eliminated, i.e., the energy is deployed where it is actually needed. [ABSTRACT FROM AUTHOR]

Abstract The main objective of this study was to develop a heat storage gypsum-cement board (G/C board) by using vacuum impregnation method and evaluate its physical and thermal properties. In particular, this paper analyzed the chemical property, heat capacity, thermal resistance, thermal conductivity, and heat transfer performance of the heat storage G/C board based on results of Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), Thermogravimetric analysis (TGA), TCi, and dynamic heat transfer analyses. Especially, dynamic heat transfer analysis was focused in this study to determine time-lag effect and thermal efficiency property of the developed heat storage G/C board with PCMs. As a result, it was confirmed that PCM containing heat storage G/C boards had time lag effect. Such heat storage G/C board could be used to reduce energy consumption of buildings. In the analysis of building energy consumption through energy simulation, the energy reduction effect is larger when the G/C board with n-octadecane was applied, because n-octadecane has more suitable phase change temperature. Especially, when n-octadecane was applied, it is effective to reduce the cooling load during cooling season. [ABSTRACT FROM AUTHOR]

Highlights • The aim of this research is to investigate the thermal performance of the prototype building to assess overheating in the summer, particularly the long-term heatwave period in the UK. • To understand the impact of energy efficient technologies on occupants' wellbeing and health in order to identify occupants' thermal comfort level in these types of dwellings. • The study adopts a quantitative research design based on indoor monitoring, in-situ measurements and dynamic simulation modelling. • This clearly indicates that due to this prototype house's structural construction material behaviour in a heatwave and its location (southwest facing orientation), it is susceptible to very high overheating risk. • The results found from the monitoring during a long-term heatwave in the summer of 2018 provided strong evidence for overheating and thereby thermal discomfort in many occupied spaces, particularly on the first floor followed by several spaces on the ground floor. Meanwhile, all the occupied spaces appeared to be well above the acceptable thermal comfort benchmark throughout the data collection period. • To calibrate the building performance, as well as the building materials and components adopted in the model, the measured indoor environment data were scrutinised in conjunction with the IES simulation results. Abstract There is growing evidence that terraced houses—thermally lightweight, well insulated, naturally ventilated with three exposed wall surfaces—are at risk of overheating, especially in south-eastern England. The aim of this study is to evaluate the building performance and develop a reliable building simulation, which will be employed in the second phase of the study: developing affordable and feasible passive design strategies to support the energy-efficient building systems of the construction industry. This paper reports on the results from the first phase of the study where a quantitative methodology, including indoor and outdoor environmental monitoring, in-situ measurements and building simulation modelling, was adopted. The performance of a case study was modelled and simulated via employing Integrated Environmental Solutions (IES) software suite. The results from the base-case were analysed according to the adaptive thermal comfort of Chartered Institution of Building Services Engineers (CIBSE) Technical Memorandum 52 guidelines: The Limits of Thermal Comfort—Avoiding Overheating in European Buildings. The spaces studied within the case study house were observed to exceed the acceptable limits of thermal comfort; particularly, the large bedroom within this zone exceeded the upper limit for overheating up to 11 h daily. Furthermore, the results from the monitoring study indicate a high risk of summertime overheating across all the case study settings, especially during short-term peaks in outdoor temperatures. The main reasons for the problematic thermal performance were identified as well-insulated and fully air-tight building fabric, the lack of sufficient ventilation through the living spaces and excessive heat gains through the composite cladding material. [ABSTRACT FROM AUTHOR]

Highlights • Studies using questionnaire for collecting energy data in buildings are reviewed. • Investigations are classified according to data collection methods and objectives. • Problems about terminology, identity of recipients, and response rate are highlighted. • The review provides suggestions and guidance on the use of questionnaire. Abstract The paper presents a review of the literature on the use of the questionnaire as a tool for collecting energy data in residential buildings. Numerous studies used the questionnaire to gather necessary information for different purposes. However, even using the same tool, the procedures differ in terms of distribution and compilation, contact with the interviewees and type of proposed questions. One hundred thirty-seven studies were considered in the review, including both scientific articles and surveys reports. The available works were analysed and classified according to the geographical origin, period, sample size, sample structure, data collection methods, response rate, data processing, and objectives of the investigation. In the 80% of the selected studies, the questionnaire is used as unique tool for data collection, albeit cases in which the questionnaire is coupled with other survey techniques, such as field measurements and time use surveys, have also been recorded. The in-person interview is the most common completion option in the sample and it is also the method that produces the highest response rate (77.6%). Generally, the collected data are subjected to statistical processing (over 80% of the cases). Regarding the objective of the surveys, the questionnaire is mainly employed for investigations on energy consumption and occupants' behaviour, but also other purposes were identified. Overall, the revised sample and the experiences reported are largely varied and heterogeneous. The lack of a homogeneous methodology appears in the use of an extremely diversified terminology. Therefore, the codification of a reference method and the standardisation of the nomenclature would be desirable. It would be useful to define general guidelines to be followed when designing surveys by using questionnaires. The review provides some suggestions and guidance on the use of questionnaire, highlighting strengths and weaknesses, and represents a source of information for future researches focused on the energy performance of buildings. [ABSTRACT FROM AUTHOR]