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Highlights • Review the existing data sources of US commercial building energy usage. • Compare energy results from survey and simulation data for US commercial buildings. • Discuss the different applications of existing data sources for US commercial building energy. • Provide the cases of applications to guide the users to use the data sources. Abstract U.S. commercial buildings consumed around 18% of total primary energy in 2017 and a 2.23 EJ increase is expected by 2050. Energy-related data for commercial buildings can be used for various applications, including benchmarking, building component analysis, market potential analysis, and policy making. Although there are plenty of data sources for energy usage in commercial buildings, they have not been thoroughly reviewed and summarized. As a result, users do not have comprehensive guidelines about selections of right data sources for specific application needs. To fill this gap, this paper conducts a comprehensive review to summarize data sources for energy usage in U.S. commercial buildings and discuss their usages for different applications. First, the paper summarizes the survey and simulation data sources for energy usage. The data sources are compared in terms of their data collection methods, released information, and relevant features. Second, this paper analyzes the applications for different survey and simulation data sources. This review categorizes the applications of data sources into five categories, including energy performance benchmarks, energy use forecasts and predictions, energy use contributions of building components, supports of building energy policies, and urban-scale energy use analysis. Moreover, the paper introduces several cases to demonstrate the usages of these data sources. [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]

[Display omitted] • Unique preconditions, specific characteristics for use can be boundary conditions. • Energy use, users' behavior, lighting are correlated conditions within buildings. • Boundary conditions can help explain the occupants' behaviour and the use of spaces. • Knowledge of boundary conditions helps to understand how to design buildings. • Boundary conditions can be building/ user/ input-values and method-specific. This paper aims to review the boundary conditions (B/C) in specific categories (energy, building use, and lighting) within non-residential buildings to pave the way to a better understanding of users' requirements and needs of the built environment. For this paper, B/C are understood as unique preconditions, specific characteristics for use, determining specific features of buildings, enabling an accurate understanding of non-residential spaces concerning energy use, user behaviour, and lighting. This paper describes the results of an overall quantitative (1st method) review and a systematic review (2nd method) of boundary conditions and their factors within different types of non-residential buildings from the users' perception. Followed by a qualitative experts' literature review (3rd method) on B/C within offices, schools, and hospitals chosen by a team of international experts working together on Subtask A: User perspective and requirements, Task 61 IEA (International Energy Agency): Solutions for daylighting and electric lighting. The first review method led to the selection of 21 papers. The second method resulted in the selection of 7 papers out of 93,143 found in Scopus; during the 3rd review, experts collectively chose 74 additional papers focussing on the users' factors contributing to specific B/C. The scope of this paper is limited only to offices, schools, and hospitals. Based on the findings, the authors recognise a broad definition of boundary conditions from specific values, and conditions to interconnected factors, user profiles, functions of the building types, and operating hours. This paper is an overview of B/C factors found in the literature that can help explain the occupants' behaviour and the use of spaces. B/C are often type of building/user/location/situation/simulation input-values and method-specific. Therefore, they cannot be widely applicable but offer patterns and help to understand the correlations between various factors shaping the built environment. A better comprehension of the reasons for identifying B/C and their factors can help in developing a deeper knowledge of how we use buildings to find optimal ways to design them. [ABSTRACT FROM AUTHOR]

This paper considers energy conservation practices in Qatar with special emphasis on commercial buildings. Energy conservation approaches are classified into five main areas and an Energy Conservation Matrix (ECM) database for buildings is developed. The ECM maps the energy conservation techniques/technologies to models versus their application domain. Three scenarios (building envelope design, change in customer behavior, and consideration of renewable energy supply) are analyzed to study different alternatives for efficiency improvement. The analysis is done in a case hotel. The analysis shows that, the energy conservation potential of using envelope redesign for the case study is about 7.5% while conservation through behavior change ranges between 2.74% and 15.80%. The conserved energy potential ranges between 10% and 24.12% of the site energy in the combinatorial scenario that integrated envelope design alternatives with customer behavior change. The renewable energy (RE) scenario conserves energy, indirectly, by using green energies generated from renewable sources. The output shows that due to changes as per the three scenarios, total CO 2 emissions of the building are also reduced. The analysis shows that adoption of 30% RE alternative can reduce emissions by about 27% with respect to the reference scenario. It is believed that the scenarios developed in this paper and the results obtained will motivate the designers to consider alternative designs or redesigns in the large scale commercial buildings. [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]

Overall energy consumption has expanded over the previous decades because of rapid population, urbanization and industrial growth rates. The high demand for energy leads to higher cost per unit of energy, which, can impact on the running costs of commercial and residential dwellings. Hence, there is a need for more effective predictive techniques that can be used to measure and optimize energy usage of large arrays of connected Internet of Things (IoT) devices and control points that constitute modern built environments. In this paper, we propose a lightweight IoT framework for predicting energy usage at a localized level for optimal configuration of building-wide energy dissemination policies. Autoregressive Integrated Moving Average (ARIMA) as a statistical liner model could be used for this purpose; however, it is unable to model the dynamic nonlinear relationships in nonstationary fluctuating power consumption data. Therefore, we have developed an improved hybrid model based on the ARIMA, Support Vector Regression (SVRs) and Particle Swarm Optimization (PSO) to predict precision energy usage from supplied data. The proposed model is evaluated using power consumption data acquired from environmental actuator devices controlling a large functional space in a building. Results show that the proposed hybrid model out-performs other alternative techniques in forecasting power consumption. The approach is appropriate in building energy policy implementations due to its precise estimations of energy consumption and lightweight monitoring infrastructure which can lead to reducing the cost on energy consumption. Moreover, it provides an accurate tool to optimize the energy consumption strategies in wider built environments such as smart cities. [ABSTRACT FROM AUTHOR]

[Display omitted] • Carbon emissions are explored in China's building operations from provincial perspective. • GDIM is employed to factorize ten affecting factors of CO 2 emissions in building sub-sectors. • CO 2 emissions of building sub-sectors are predicted with dynamic simulation. • The contribution of provinces to the change and peak of national emissions are discussed. Carbon emissions from building operations have a critical impact on achieving carbon peaking goal in China. However, the provincial emission performance in building operations are quite different and it is still unidentified how they affect the national emission change and peaking target, making national emission reduction target hard to implement at the provincial level. This paper explores the driving factors leading to emission changes and examines the prospective emission peaks in China and China's provinces by adopting generalized Divisia index method (GDIM) and the combination of scenario analysis and Monte Carlo simulation. Furthermore, the regional emission heterogeneity and the contribution of provinces to the change and peak of national emissions are discussed to identify regions and provinces that should be focused on emission monitoring. Results show that in China and most Chinese provinces, energy consumption of commercial buildings, energy consumption and floor space of urban residential buildings besides income improvement, and the per capita CO 2 emissions of rural residential buildings contributed greatly to emission growth. Under current policies, commercial and urban residential buildings in most provinces would fail to reach their emission peaks ahead of schedule. The urban residential buildings in the northern region, commercial and rural residential buildings in the eastern region caused higher CO 2 emissions. At the provincial level, Shandong, Shanxi and Hebei made the top contribution to total emissions of commercial, urban residential and rural residential buildings, respectively. Shandong and the provinces in north China of urban residential buildings and four provinces including Shandong, Guangdong, Hebei and Zhejiang of commercial and rural residential buildings had the significant impact on the uncertainty of the peaking time of CO 2 emissions. These findings provide a strong decision-making reference for Chinese government to formulate targeted regional emission reduction policies in building operations. [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]

• A mathematical model to calculate the LCOS for reversible fuel cells and Li-ion batteries. • Techno-economic model for a standalone PV solar system in green energy buildings. • Uncertainty and simulation cost analyses of energy storage systems used in green buildings. Energy storage systems (ESSs) were introduced to overcome the risks posed by energy curtailment. In this paper, we compare the Levelized cost of storage (LCOS) for PEM Reversible Fuel Cells, Solid Oxide Fuel Cells, and Lithium-Ion Batteries in three different locations in the United States (Tucson, Seattle, and Rochester) that are characterized by different energy consumption profiles. This research examines three commercial buildings of different sizes: small, medium, and large, with three occupancy profiles: low, medium, and high energy consumption in each of the three locations. The objective is to quantify the impact of uncertain demand and uncertain financial parameters on the LCOS. The results show that all ESSs can represent an attractive alternative to energy curtailment regardless of the building size or the occupancy profile. Further, the results show that fuel cells are a better fit for demand uncertainty as they are more resilient to changes and remain attractive despite that. The uncertainty analysis showed that the minimum LCOS was $0.049/kWh, and it was for a PEM-RFC in a small building in Tucson under high energy consumption behavior. Oppositely, the maximum LCOS was $0.679/kWh, and that was for a large building in Seattle under high energy usage occupancy as well. [ABSTRACT FROM AUTHOR]

Occupants affect energy consumption in buildings by contributing internal heat gains, increasing internal carbon dioxide levels and adapting their behaviour. Estimated occupancy schedules are used in building energy models for regulatory compliance purposes and when empirical data are not available. Metadata, such as personal location data, is now collected and visualised on a global scale and can be used to create more realistic occupancy schedules for non-domestic facilities, such as large retail outlets. This paper describes a protocol for extracting and using freely available metadata to create occupancy schedules that are used as inputs for dynamic simulation models. A sample set of twenty supermarket building models are used to demonstrate the impact metadata schedules have when compared with models using the estimated schedules from regulatory compliance. Metadata can be used to create bespoke occupancy profiles for specific buildings, groups of buildings and building archetypes. This method could also help reduce the gap between predicted and actual performance. In the example models, those using the regulatory compliance schedules underestimated heating demand by approximately 10% and overestimated cooling demand by over 50% when compared to models using the metadata schedules. Although this work focuses on UK facilities, this methodology has scope for global application. [ABSTRACT FROM AUTHOR]

• A new urban building energy modeling tool-AutoBPS was developed. • AutoBPS calculated urban building energy demands, analyzed energy retrofit and rooftop PV potential via EnergyPlus. • AutoBPS automatically generated multi-zone models, considering shading from surrounding buildings. • A downtown district of 3633 buildings in Changsha, China, was simulated to demonstrate the tool's features. • All three energy conservation measures and PV installation could save 38.6% of total energy demand. China has promised to peak carbon emissions by 2030 and become carbon neutral before 2060. Achieving this target requires actions from all sectors, especially the building sector, which accounts for 33 % of the total energy consumption in China. Within this scenario, this paper introduces a new urban building energy modeling tool, Automated Building Performance Simulation (AutoBPS), to calculate urban building energy demands and to analyze energy retrofit and rooftop photovoltaic (PV) potential. AutoBPS uses a building-by-building approach to automatically generate multi-zone (one zone per floor) models based on the city building dataset, considering the shading effect by surrounding buildings. The tool simulates building energy use and PV generation via EnergyPlus. A downtown district of 3633 buildings in Changsha, China, was selected as a case study to demonstrate the features of the tool. Firstly, annual baseline energy uses of 1793 GWh were simulated by inputting Geographic Information System (GIS) data. Then, different energy conservation measures (covering envelope, lighting, cooling system upgrade, etc.) and roof PV generation systems were adopted to explore energy saving potential. The results showed that the window upgrade was the most energy-efficient individual measure for residential buildings, while the lighting upgrade was that one for commercial buildings. Altogether three measures could save 2.7 ∼ 30.7 % of annual site energy use intensity per building. The total energy demand could be reduced by 18.5 % by combining all measures and saved by 38.6 % when PV installation was added. AutoBPS can be easily applied to other cities, supporting urban energy efficiency plans for carbon reduction goal implementation. [ABSTRACT FROM AUTHOR]

• Freely available online overview of district heating efficiency indicators. • Hourly district heating energy use data from approx. 43 000 buildings. • Building stock segmented into building typologies for analysis of: • -1) Heating energy use intensity, 2) load variation and 3) cooling efficiency This paper provides a state-of-the-art and freely available online overview of the district heating (DH) efficiency of Danish building typologies. The overview is based on DH consumption data from 42 969 buildings constructed up until 2017 that in combination with data from the Danish Building and Dwelling Register constitutes a dataset with a previously unseen level of detail, confidence, sample size, and research opportunities. The paper also presents an initial analysis of the dataset in terms of three heating efficiency indicators, namely 1) Annual heating energy use intensity, 2) Daily heat load variation, and 3) Cooling efficiency. The overall finding was that building typologies perform significantly different from one another concerning indicators 1 and 2, but not for indicator 3. It is therefore not possible to declare one building typology more efficient than the other as this depends on the building construction year and the considered efficiency indicator. The data indicate that further energy-efficiency improvements of the existing building stock are essential to the realization of 4th Generation District Heating technologies in Denmark and that these improvements most likely will lead to increased heat load variations that need to be addressed, for instance by enabling a larger degree of demand-side flexibility in the existing district heating systems. The large temporal variation of the buildings' cooling efficiency indicates that there is a potential for rethinking e.g. domestic hot water production in the summer or introduce differentiated billing based on the daily, weekly, or maybe seasonal cooling performance. The presented dataset can be subject to several future investigations which are also discussed in the paper. [ABSTRACT FROM AUTHOR]

• EU policy efforts on energy efficiency in buildings stared in the 1970s in response to the oil crisis. • The first comprehensive EU policy was the SAVE directive in 1992, introducing policy actions still relevant today. • A major step forward was the Energy Performance of Buildings Directive in 2002 and its subsequent amendments. • Mandatory energy performance standards are progressively converging towards near zero energy buildings. • Additional policies and financing are needed for the full decarbonisation of the building stock. The reduction of energy demand in buildings through the adoption of energy efficiency policy is a key pillar of the European Union (EU) climate and energy strategy. Energy efficiency first emerged in the EU energy policy agenda in the 1970s and was progressively transformed with shifting global and EU energy and climate policies and priorities. The paper offers a review of EU energy policies spanning over the last half century with a focus on policy instruments to encourage measures on energy efficiency in new and existing buildings. Starting from early policies set by the EU in response to the Oil Embargo in the 1973, the paper discusses the impact of EU policies in stimulating energy efficiency improvements in the building sector ranging from the SAVE Directive to the recently 2018 updated Energy Performance of Buildings Directive and Energy Efficiency Directive. The review explores the progress made over the last 50 years in addressing energy efficiency in buildings and highlights successes as well as remaining challenges. It discusses the impact of political priorities in reshaping how energy efficiency is addressed by EU policymakers, leading to a holistic approach to buildings, and provides insights and suggestions on how to further exploit the EU potential to save energy from buildings. [ABSTRACT FROM AUTHOR]

The variable air volume (VAV) system is the most popular form of heating, ventilation, and air-conditioning (HVAC) system used in commercial buildings. Researchers and engineers often use VAV systems as a reference when evaluating new technologies and systems or comparing design options. However, VAV system performance varies significantly, in part because of variations among VAV system controls, so, when analyzing use cases, it is critical to accurately represent system controls in order to accurately define system performance. Unfortunately, no existing literature documents standard VAV system controls for this purpose. This paper aims to remedy this omission by characterizing the variations in VAV system controls and proposing an approach to representing VAV system baseline performance. We used EnergyPlus to model variation among VAV system controls. We use the medium-size office reference-building model developed by the U.S. Department of Energy to demonstrate the impact of variations among controls in two U.S. climate zones and sort system performance into “good,” “average,” and “poor” categories. [ABSTRACT FROM AUTHOR]

Because the building sector has become a huge energy consumer, providing useful approaches can help tackle this important issue. Windows are an important component of a building since most of the natural light inside the building is provided by passing this component. Improper design of windows and their associated components, such as shadings, overheats the space and increases energy consumption, and on the other hand, disturbs the visual and thermal comfort of the building's occupants. The present paper aims to present a new approach for multi-objective optimization of the architectural specifications and control parameters of a smart shadow curtain. The use of this proposed approach leads to a significant reduction in building energy consumption and improved thermal and visual comfort. Energy-related simulations are performed through EnergyPlus software. Afterward, 21 design variables including control strategies and setpoints, location and orientation of shading, slat dimensions and material specifications, as well as hourly slat angle are considered in JEPLUS software. These simulations are conducted in four geographical directions over seasonal and annual periods for the city of Tehran, Iran. JEPLUS + EA software is employed to perform sensitivity analysis on the outputs of JEPLUS software and data optimization. Morris sensitivity analysis is implemented to investigate the effects of input variables on the objective functions. The NSGA-II algorithm is then applied to optimize the data to extract the optimal points on a Pareto front. The optimization results indicate that the external blind performs better than the internal blind and also the smaller the angle of the blind slat is, the better the visual comfort is but increases the electricity consumption for lighting. A comparison of the results of multi-objective optimization of controlled blind characteristics with initial values demonstrates that based on geographical directions, the total energy consumption of the entire building decreases by a range of 40–50 % per year, while visual comfort and thermal comfort are improved by ranges of 70–100 % and 10–40 %, respectively. [ABSTRACT FROM AUTHOR]

• POE was carried out to compare the design target and operational effect after use. • A Large Irregular Green Commercial Building was chosen for in-depth analysis. • We recommend the building energy saving and carbon emission reduction strategy. Commercial buildings, especially large irregular commercial buildings with complex space and dense occupation, have high energy consumption and carbon emissions. Moreover, the actual operating performance differs significantly from its design, in terms of energy conservation and carbon emissions. This study selected an irregular large-scale green commercial building in Dalian, a coastal city in the cold zone of China. The running energy consumption and indoor physical environment were investigated and analyzed, and a satisfaction survey was conducted using the Post-occupancy evaluation (POE) methods. The promotion strategies of holistic design were proposed by comparing the operational building performance with the design target. The research shows that the energy consumption of the building was 68.02 kWh/m2, which failed to meet the standard requirements. The carbon emission was 74.76 kgCO 2 /m2. Less than 10 % of humidity in summer fell in the expected range, and the lowest value of PM 2.5 concentration in winter was 28.3 %. The score of indoor environmental quality and service satisfaction was low. This paper identifies the problems and analyzes the differences between the operation stage and design phase. It provides recommendations and guidelines for optimizing the carbon emission and indoor environment based on the performance improvement of building operational conditions. [ABSTRACT FROM AUTHOR]

• New building development projects underperform often. • Underperformance leads to increased energy consumption and CO 2 emissions in building operation. • The paper links building project development and building operational performance. • Two simulation methods are combined in a multi methodology framework in the paper. • The paper explores the implications of project partner collaboration for operational building performance in the case of a recent building project. The "performance gap" in the United Kingdom construction industry is a persistent problem as new building development projects underperform more often than not. The "performance gap" is partially attributed to the number of stages involved in building project development and the coordination difficulties of partners with different incentives. The project outcome is important for energy consumption, carbon emissions and occupant well-being. Thus, it is important to study the project management process in terms of the standard time, cost and quality parameters, but also in terms of project partner incentives and coordination, and the subsequent energy performance and resultant indoor environmental conditions. A system dynamics model of project management processes is developed to explore the implications of partner coordination for building quality. The system dynamics model is coupled to a building performance simulation model to explore building energy consumption and Indoor Environmental Quality, and apply this on a recent building project case study. Results show that greater project partner alignment can reduce annual energy consumption up to 12% and CO 2 emissions up to 37%, with greater emphasis in the design stage of the project subject to resource availability. The trade-offs involved on value appropriation are considered and discussion of results points to possible ways for improvement. [ABSTRACT FROM AUTHOR]

• A POE-integrated simulation method is proposed to address energy occupant behavior. • Opening windows and operating AC were found to co-exist which affects simulation accuracy. • The new method improves energy simulation accuracy by 14% for domestic areas. • The method improves energy simulation accuracy by 16% for total building areas. Occupant behavior plays a critical role in building energy consumption, particularly in residential buildings. However, occupant behavior is complicated and varies significantly from case to case. Also, energy modeling of high-rise buildings is far less explored than that of low- or medium-rise buildings. This paper aims to improve accuracy in building energy simulation by utilizing Post Occupancy Evaluation (POE) data to calibrate energy model. Drawing on a review of the literature of occupant behavior and building energy modeling, the paper provides a calibration method of integrating POE data into an energy model, which is demonstrated using a real-life typical 40-storey residential building in Hong Kong. The developed method addresses seven updated input parameters, namely, schedule, devices, air-conditioners, windows, lights, domestic hot water, and cooking. By comparing the two energy modeling processes, i.e. with and without POE input, and their resultant estimated energy consumption, the paper quantifies the impact of occupant behavior on building energy consumption. Annual metered data together with energy bills obtained from the POE were utilized to validate the models. The results show that the use of the developed POE-integrated method helped to improve accuracy in energy consumption prediction by 14% for the total building floor area and by 16% for the total residential area. From examining the impacts of the seven input parameters, the paper reveals the key energy use sensitive occupant behaviors within high-rise residential buildings in Hong Kong. These include: the number of residents in each unit; adoption of window type and split type of air-conditioners; window and air condition operation modes; cooking time on weekdays and weekends; and time spent on hot water showering. The developed method can assist building designers and services engineers to estimate building energy use more accurately and provides a scenario analysis tool for clients and facility managers to develop effective energy conservation strategies. [ABSTRACT FROM AUTHOR]

• Implementation of a model predictive control scheme for HVAC systems is presented. • The hardware/software solution is generalisable to most building management systems. • Cloud solutions can offer low-cost, scalable control in commercial buildings. • Economic MPC reduced energy usage by 1.7% compared to baseline control. • Building thermal performance strongly affects viability of MPC schemes. Many businesses are looking for ways to improve the energy and carbon usage of their buildings, particularly through enhanced data collection and control schemes. In this context, this paper presents a case study of a food-retail building in the UK, detailing the design, installation and cost of a generalisable model predictive control (MPC) framework for its Heating, Ventilation and Air Conditioning (HVAC) system. The hardware/software solution to collect relevant data, as well as the formulation of the MPC scheme, is presented. By utilising cloud-based microservices, this approach can be applied to all modern building management systems with little upfront capital, and an ongoing monthly cost as low as $6.39/month. The MPC scheme calculates the optimal temperature setpoint required for each Air-Handling Unit (AHU) to minimise its overall cost or carbon usage, while ensuring thermal comfort of occupants. Its performance is then compared to the existing legacy controller using a simulation of the building's thermal behaviour. When simulated across two months the MPC approach performed better and achieved the same thermal comfort for a lower overall cost. The economic optimisation resulted in an energy saving of 650 kWh, with an associated cost savings of $240 (an improvement of 1.7% compared to the baseline), while the carbon optimisation gave negligible CO 2 savings due to the inability of the building to shift heating to low-carbon periods. Findings from this study indicate the potential for improving building performance via MPC strategies, however the level of impact will depend on specific building attributes. [ABSTRACT FROM AUTHOR]

Utility incentive programs are an important channel to support the deployment of energy efficiency in buildings. To date, these programs have largely been limited to single-component strategies. However, many utilities are now motivated to identify and develop multi-component system retrofits to achieve deeper energy savings, which are essential to achieving broader energy and greenhouse gas reduction goals in the buildings sector. In this paper we present the energy savings, demand reductions, and cost-effectiveness of 16 systems retrofit packages in six utility regions in the United States. These results are being used by these utilities to inform and develop incentive programs for systems retrofits. Our analysis shows that packages with proven lighting and HVAC measures can provide 5–22% whole building annual energy savings, and 13–22% annual energy costs savings, using utility incentive program baselines (code and existing building). The packages are reasonably cost effective for replace-on-burnout but generally not for a retrofit scenario prior to end of equipment life. Demand response can increase both the energy savings and energy cost savings, further improving the cost effectiveness of these packages. We analyzed the impact of using existing building vs. code baselines for calculating savings, showing that the choice of baseline in developing utility incentive programs has a substantial impact on the attributable energy savings to a program, with significant implications for the overall viability of a program (generally savings against existing building condition are higher and improve project and program cost-effectiveness). [ABSTRACT FROM AUTHOR]

• Ownership structure of small commercial buildings makes energy retrofits challenging. • Retrofit package for Zero Net Energy small offices in Mediterranean climate. • Energy use and occupant comfort evaluated experimentally, side-by-side, in real time. • 65%-68% energy savings for cooling season; 22%-25% for heating season. • Energy savings are achieved while maintaining visual and thermal comfort. Making buildings zero-net energy (ZNE) is one of the major strategies for achieving carbon emission reduction goals. For this strategy to be successful, it entails a very significant reduction in energy use – 50% or more. In small commercial buildings, however, owners and building management teams usually have limited resources for identifying, analyzing, and procuring appropriate retrofit measures for reducing such use. An approach to overcome this limitation is the development of bundles of energy efficiency measures that can be presented to building owners/operators as a comprehensive package. The research presented in this paper focuses on an experimental evaluation of the impacts of a retrofit package developed for small office buildings in California. Performing this evaluation in a full-scale whole-building integrated systems test facility allowed a side-by-side evaluation in real time, against a reference case, of the impact of the retrofit package not only on energy use but also on visual and thermal comfort. The retrofit package evaluated is comprised of a combination of HVAC, lighting (including daylighting), and plug load measures. The evaluation occurred at different times of the year in order to account for seasonal variations in environmental conditions, including solar angles and weather. The experimental facility allowed testing for two different façade orientations: south and west. Results show that the proposed ZNE retrofit package can save significant amounts of energy for small commercial buildings. During cooling-prevalent periods, total energy savings were 65% for south orientation and 68% for west orientation; during heating-prevalent periods total energy savings were 22% for south orientation and 25% for west orientation. Measurements indicate that the ZNE retrofit package resulted in small but not very significant changes in comfort levels for building occupants. [ABSTRACT FROM AUTHOR]

• Measured data quantifies savings from OCC and sequence of operations improvements. • Energy model used to parse out savings contributions of each control intervention. • Occupancy-based ventilation ineffective without supply air temperature reset. • Use of an economizer high limit returns marginal cooling savings with little effort. • Use of OCC magnifies savings from the changes to control sequences considered. Partial occupancy of commercial offices has become the norm in the wake of the COVID-19 pandemic. Given this, occupant-centric control (OCC), which adapt building systems based on occupants' presence or preferences, offer an alternative to traditional control that assumes full occupancy. However, poor sequences of operation can degrade the benefits of OCC. This paper explores this interaction by examining energy data from two buildings – one with two control logic faults corrected and an occupancy-based ventilation OCC implemented in 2020, and one with traditional ventilation – from 2019 to 2020. Sequences that impacted implementation in the first building are discussed. Then, a calibrated energy model of the second building is developed to evaluate how occupancy-based ventilation alongside changes to the sequences of operation – namely supply air temperature (SAT) reset and economizer high limits – impacted energy use. The inclusion of OCC and improved sequences in the second building saved 30.6% and 9.6% of annual heating and cooling energy, respectively. Without an SAT reset, OCC saved 4.4% and 3.9% of heating and cooling, respectively, compared to 15.7% and 5.7% when an SAT was present. These results begin to characterize the relationship sequences of operation and OCC implementations have with one another in commercial offices. [ABSTRACT FROM AUTHOR]

• Miscellaneous loads are a major opportunity to save electricity in vacant buildings. • A method for inferring vacancy was developed. • The model generates a continuously updated likelihood of vacancy. • The vacancy inference model requires less ground truth data. • The sensor fusion approach is robust and enables flexibility. Recent events have forced building managers to examine energy use during vacant periods and revealed miscellaneous electrical loads (MELs) as an opportunity for savings. This paper addresses a key step in unlocking these savings, specifically the reliable identification of when a building is vacant. A Vacancy Inference Engine (VIE), using sensor fusion, was developed to identify vacant periods based on outputs from common sensors, historical building vacancy patterns, and expert knowledge. The VIE calculates the confidence that a building is vacant, allowing building managers to balance the capture of energy savings with the possibility of complaints due to powering down MELs. The VIE has the advantage over logistic regression and other models in that it does not require a full set of ground truth for the training process. The VIE successfully predicted vacancy in an office building using input data streams of instantaneous electricity demand, indoor carbon dioxide concentrations, and the number of active Wi-Fi connections. The VIE's ability to predict vacancy was compared to that of logistic regression using a metric based on the Complaint Opportunity Rate and found to be nearly identical (0.94 versus 0.95, respectively). [ABSTRACT FROM AUTHOR]

A large part of energy consumption in homes, offices and commercial spaces is related to Heating, Ventilation and Air-conditioning (HVAC) devices. The effective parameter on the consumption of HVAC systems is internal heat gains that arise from occupants, electric equipment and lighting. In order to reduce the energy consumption of these systems, internal heat gains should be predicted accurately. Since there are few investigations performed on the prediction of internal heat gains, in this paper, three predictive models, namely multiple regression model, Levenberg–Marquardt back-propagation (LM-BP) model and similar days method based on combined weights, have been deployed. By assessing the influential factors on internal heat gains, fundamental theories, structures, equations and parameters of these models are thoroughly proposed. To examine the prediction techniques, an office building in China was considered. It was found that all the proposed models have high accuracy; however, the LM-BP neural network showed the most precision among other models with RMSE = 15.59, MAE = 10.16 and MAPE = 6.35. This model had a higher agreement with the actual internal heat gains compared to the predetermined working programs in the ASHRAE standard 90.1. The proposed models used in this study can lead to providing a theoretical base for scholars and engineers to improve the predictive control of HVAC systems, which plays an important role in enhancing thermal comfort, saving energy of residential buildings. [ABSTRACT FROM AUTHOR]

• A method is to disaggregate bulk meter data of commercial buildings into end uses. • The method uses building automation system data to disaggregate bulk energy use. • The method is demonstrated with data from a submetered academic office building. • The method accurately disaggregated the heating and electricity end uses. Although understanding end uses in large commercial and institutional buildings is of great utility for achieving energy efficiency, most new and existing buildings still lack adequate submetering except for large end uses. End use disaggregation methods offer an alternative to fulfill weaknesses in a metering network, especially in large commercial and institutional buildings. Unlike non-intrusive load monitoring (NILM) methods that mostly rely on high frequency power consumption data and focus on residential buildings, this paper introduces an end-use disaggregation approach using building automation system (BAS) data. The BAS data provide information about the operational status of main energy-consuming systems and equipment such as fans, pumps, air handling unit (AHU) heating coils, and variable air volume (VAV) terminal reheat coils. The proposed method uses a suite of multiple linear regression models estimated by the genetic algorithm and a least-square solver to disaggregate heating and electricity use data using BAS data as predictors. The method was demonstrated with data from an academic office building in Ottawa, Canada. The accuracy was evaluated by comparing the disaggregation results to ground truth energy use data acquired from dedicated submeters. The findings suggest that the method can accurately disaggregate building-level hourly electricity and heating energy use into following end use categories, for electricity: (a) occupant-controlled loads (lighting and plug loads), (b) distribution loads (the electricity used by the AHUs); and for heating: (a) energy used by the AHU heating coils, (b) perimeter heating devices (reheat coils/and radiators). [ABSTRACT FROM AUTHOR]

In order to reduce CO 2 -emissions it is necessary to reduce the energy use in the existing building stock significantly. Gadehavegård – a social housing built-up area consisting of 19 similar blocks of flats with nearly 1000 dwellings situated in Denmark – needed renovation and therefore a block was selected for testing an ambitious renovation that would result in a significant reduction in energy use and CO 2 emissions. The ambition was to reach the German Passivhaus standard for the building, i.e. a very strict requirement, especially for a renovation project. The renovation included insulating the facades from the outside, replacing all windows, insulating the roof, installing decentralized mechanical ventilation systems with efficient heat recovery and a photovoltaic system on the roof. In addition, the balconies were included in the apartments by installing foldable glass façades. This paper gives a detailed description of the renovation project along with measurements of the energy use and indoor climate before and after renovation. Comparing the achieved results to the Passivhaus requirements show that the original goal is not achieved, however, the building fulfils the less strict requirements of the Passivhaus renovation certification EnerPHit and is still a very good example on how significant reductions in energy use can be achieved for these types of buildings. Results before and after renovation are compared using the energy signature and shows that heating energy consumption has been reduced by more than 50% even though indoor temperature on average has increased from 21.7 °C to 23.3 °C. [ABSTRACT FROM AUTHOR]

• A comprehensive analysis of data from commercial buildings in United States to identify operational problems and opportunities. • These opportunities were not strongly correlated with building vintage, size, or building type, but were reflective of how the buildings are operated. • This analysis showed that opportunities to improve building operations exist in almost every building. • There was wide variation in whole building energy savings, ranging from 0 to 50% and 0 to 18 $/m2 with median percent annual whole building savings of 12% and median normalized annual cost savings of $1.75/m2. In the United States, as much as 30% of the 19 EJ that commercial buildings consume is considered excess. Much of the excess energy is due to the inability to manage building operations efficiently. Because almost 20% of the total primary energy consumption is associated with commercial buildings, significant energy reductions in this sector are needed to mitigate climate change. Therefore, many cities and states are mandating periodic "tune-ups" of these buildings to eliminate excess energy consumption. Although the benefits of tune-ups and retro -commissioning are clear, focusing these mandates to look for specific opportunities has been a challenge because of the lack of studies that document the prevalence of opportunities. Therefore, we analyzed building automation system data from 151 buildings across the United States to document common operational problems and opportunities to improve building operations. This analysis showed that opportunities to improve building operations exist in almost every building. These opportunities were not strongly correlated with building vintage or size, but were reflective of how the buildings are operated. The prevalence of the top 20 opportunities ranged between 74% and 23%, with 40% of these associated with air-handling units. The rest of the opportunities are associated with schedules, chilled and hot-water distribution, and zone controls. Of the 151 buildings, 69 of them implemented corrective actions of some or all opportunities that were identified. Implementation varied across the Re-tuning categories, with 60% for schedule opportunities, 50% for zone opportunities, over 40% for the air-handling unit and hot-water opportunities, and 35% of the chilled-water opportunities. There was wide variation in whole building energy savings, ranging from 0 to 50% and 0 to 18 $/m2 with median percent annual whole building savings of 12% and median normalized annual cost savings of $1.75/m2. In addition to documenting these key findings, the paper provides a list of opportunities that can be automatically and continuously identified and corrected and offers a list of those opportunities that should be the focus of the mandates. [ABSTRACT FROM AUTHOR]

• The degree of influence of residential cluster layout factors on building heating energy consumption levels in cold regions of China. • Provides an idea of residential layout optimization and design based on the energy saving objectives of residential clusters, translating quantitative indicators such as wind environment into residential layout form design problems. • Energy simulation-based residential planning and layout design method is used to quantify the relationship between residential cluster design factors and energy efficiency in Jinan, resulting in optimal design parameters for residential planning factors in Jinan. • The study of building heating energy consumption in a city in a cold region of China, with a methodology that can be used in countries or cities with similar climate characteristics. Residential heating energy consumption in China accounts for about 34% of the total building operation energy consumption in the country. The layout pattern of residential clusters is one of the most critical factors affecting building heating energy consumption, yet few scholars have studied the layout of residential clusters in a quantitative way. This paper takes Jinan, a typical city in cold region IIB of China, as an example, to quantify and analyse the residential layout parameters related to heating energy consumption: group layout, building orientation and building spacing; ECOTECT and HTB2 are used to model and simulate the energy consumption of the three types of layout residential groups selected for the study, and the relationship between residential group layout parameters and residential heating energy consumption is analysed based on multiple linear regression analysis. The relationship between residential group layout parameters and residential heating energy consumption was analysed based on multiple linear regression analysis. The study shows that, on the premise of ensuring the same building density, the group layout has the greatest influence on the building energy consumption, and the courtyard layout has lower heating energy consumption compared to the row and staggered layout; on this basis, the influence of building orientation and building spacing on heating energy consumption is discussed. The study shows that increasing the spacing between buildings to meet the daylight spacing will reduce energy consumption by 4% and that a 15° shift to the west will reduce energy consumption by 5%. This result confirms the importance of cluster layout in terms of heating energy consumption and urban energy efficiency in cities in cold regions. [ABSTRACT FROM AUTHOR]

[Display omitted] • The research investigates urban clusters of tall commercial buildings in Tallinn. • A workflow integrating thermal models of indoor and outdoor domains is developed. • Indoor comfort and energy consumption of tall commercial buildings are analyzed. • First-time assessment of outdoor thermal comfort in commercial areas in Tallinn. • The research suggests building layouts for improving indoor and outdoor comfort. Considering climate change, controlling outdoor microclimates is an increasingly pressing concern. Microclimates have a significant effect on both outdoor and indoor comfort, and on the energy efficiency of buildings. This concern is particularly important as current climate conditions reveal that warmer summers are threatening the comfort of pedestrians and causing overheating in office environments, which is consequently increasing cooling energy consumption. A further concern is that this trend now extends to Nordic latitudes. Existing literature demonstrates how a local microclimate depends on many factors such as urban density, shape and orientation of buildings, the types of materials present, the number of green areas and anthropogenic activities. However, there is little research focusing on how reciprocal distances among tall buildings, and their relative position, affect outdoor and indoor comfort, and the associated energy consumption of buildings. This paper presents a unique and comprehensive insight into the interconnected nature of indoor and outdoor comfort via coupled simulations. It presents a study of clusters of tall commercial buildings located in the Nordic climate of Tallinn (Estonia) with different microclimates, and shows that the differences are due to variable shadowing and reflections and different wind patterns. The results, which focus on summer conditions, show that small variations of cluster layout strongly affect the local indoor and outdoor comfort, thus highlighting the need to conduct both studies simultaneously in research aiming to increase pedestrian and indoor comfort and resource efficiency. [ABSTRACT FROM AUTHOR]

[Display omitted] • Building energy consumption is largely driven by diversity in occupant behavior. • Machine learning can be used to predict lighting energy use with occupancy data. • Building layouts are optimized with a naïve clustering method and genetic algorithm. • Layout optimization can yield 5–6% reduction in lighting energy use. One of the primary driving factors in building energy performance is occupant behavioral dynamics. As a result, the layout of building occupant workstations is likely to influence energy consumption. In this paper, we introduce methods for relating lighting zone energy to zone-level occupant dynamics, simulating energy consumption of a lighting system based on this relationship, and optimizing the layouts of buildings. The optimization makes use of both a clustering-based approach and a genetic algorithm, and it aims to reduce energy consumption. We find in a case study that nonhomogeneous behavior (i.e., high diversity) among occupant schedules positively correlates with the energy consumption of a highly controllable lighting system. We additionally find through data-driven simulation that the naïve clustering-based optimization and the genetic algorithm (which makes use of the energy simulation engine) produce layouts that reduce energy consumption by roughly 5% compared to the existing layout of a real office space comprised of 151 occupants. Overall, this study demonstrates the merits of utilizing low-cost dynamic design of existing building layouts as a means to reduce energy usage. Our work provides an additional path to reach our sustainable energy goals in the built environment through new non-capital-intensive interventions. [ABSTRACT FROM AUTHOR]

• Several control strategies are developed for attic switchable insulation systems. • A systematic analysis is carried out to evaluate these controls for US homes. • Switchable insulation systems can save up to 88% of heating/cooling energy needs. This paper summarizes the main findings for evaluating the effectiveness of simplified and advanced control strategies to operate switchable insulation systems applied to the attic of US residential buildings. Specifically, the evaluation analysis is performed for five US representative climates to determine the performance of several control options for dynamic insulation systems to minimize the heating and cooling energy end-uses for US homes. The control strategies considered include scheduled settings, temperature-based rule sets, and optimal control schemes. The comparative analysis results clearly indicate that the switchable insulation systems even when only applied to attics have significant potentials in reducing the cooling energy use of US residential homes especially those located in mild climates. Even when operated with simplified controls such as time-scheduled controls and temperature-based rule-sets, dynamic insulations are capable of providing substantial energy savings. In particular, controls using rules based on mean air and sol-air temperatures result in cooling energy savings ranging from 8% to 43% for all US climates. When optimal controls are used to operate attic dynamic insulation systems, up to 40% additional savings in cooling energy can be achieved compared to the best simplified controls at least when the building is located in Golden, CO. [ABSTRACT FROM AUTHOR]

• Reviewing limitations of previous data-mining based FDD methods on HVAC systems. • Knowledge-based and model-based systems have limitations in complicated FDD process. • In FDD in large-scale HVAC systems, driven methods are superior to other methods. • Hybrid methods have great potential in finding naïve faults in FDD process. • Developed hybrid methods can overcome delays, and get less false and missed alarms. Abnormal operation of HVAC systems can result in an increase in energy usage as well as poor indoor air quality, thermal discomfort, and low productivity. Building automated systems (BAS) collects a massive amount of data related to the operation of each component of HVAC systems. Although BAS has been implemented in many buildings over the past decade, the collected data have not been analyzed thoroughly. Some studies have relied on data-mining methods to predict, detect, and diagnose faults in HVAC systems. This paper critically reviews the existing literature and identifies the research gaps in data-driven data mining fault detection and diagnosis (FDD) methods studies on HVAC systems. In this review, data-driven based FDD methods are classified into three classes, namely supervised, unsupervised, and hybrid-learning methods. The hybrid approaches are introduced as the preferred methods among the existing approaches to be used in online FDD processes. Furthermore, some components of HVAC systems and their potential faults are discussed in detail. The outcome of this review shows that data-driven based approaches are more promising for the FDD process of large-scale HVAC systems than model-based and knowledge-based ones. Moreover, an optimal approach could involve both supervised and unsupervised learning (hybrid methods). [ABSTRACT FROM AUTHOR]

• A local sensitivity analysis of double skin facades' (DSFs) features is presented. • Simulations are carried out with two building energy simulation tools and compared. • In-built models for DSF (climate façade) are compared with experimental data. • The ranking and magnitude of sensitivity indices are similar for the two tools. • Glazing thermal and optical transmittance are the most relevant variable in DSF design. Double skin façades (DSFs) are considered façade technologies that can reduce energy use and improve occupant comfort due to their advanced features. Their design requires reliable simulations due to their complex thermophysical behaviour, which are often carried out by practitioners using building energy software (BES) tools. Using an exhaust-air façade (also called climate façade) case study, the paper analyses the sensitivity of in-built DSF models in two popular BES tools (EnergyPlus and IDA ICE) for different orientations and climates. Small variations in input variables were considered to identify the parameters that the designer should pay most attention to during the design of the DSF according to different performance indicators. The results show that, regardless of the climate or orientation, the optical properties of the system (glazing and shading) were the most important in determining its performance, followed by the thermal properties of the glazing, while the geometrical, airflow and frame characteristics were less relevant. The model validation process also showed how differences in the in-built models (i.e. the use of a capacitance node for the glazed layers) lead to a difference in the reliability of the two BES tools. [ABSTRACT FROM AUTHOR]

• Wall-type heat recovery units are solution for improving ventilation in refurbishment. • Assessment of air stream mixing by visualization and CO 2 measurements was conducted. • The risk of air mixing when using wall-type heat recovery units is low. • The analysis of the annual energy demand for residential buildings was conducted. • Wall-type heat recovery units are helpful in achieving low energy buildings. In the cold and moderate climates the energy demand for heating of fresh ventilation air is crucial. The mechanical ventilation systems with heat recovery are used to decrease the peak power of the heating system and to save the energy. In a case of wall-type heat recovery units there is a doubt about the possibility of mixing fresh and exhausted air in the combined intake-outtake device. In this paper experimental investigations were conducted to assess their hygienic safety. The original set-up containing test chamber and the equipment for CO 2 concentration measuring has been used. Visualization of airflows mixing with the usage of smoke is presented. The results show that the risk of the exhausted air return to the building is low. At the same time the ventilating efficiency of such systems is high. The analysis of the annual energy demand for the sample single-family and multi-family buildings has been conducted to present the possible energy benefits of usage such devices. Concluding, the wall-type heat recovery units should be considered as hygienic safe and helpful devices in achieving the standard of low energy buildings in a case of existing and refurbished ones. [ABSTRACT FROM AUTHOR]

• A novel method based on clustering, building energy models and Bayesian techniques is presented. • The approach enables the calibration of model parameters for large groups of buildings at an urban scale. • Various data-driven techniques have been tested acting as proxy of the simulation engine. • Three different surrogate structures have been investigated with comparison of the results. • The calibrated models provide realistic results compared to current literature studies. Prediction of building energy use, when performed at urban scale, is influenced by the choice of modelling approach, as well as the quality of available data. In the case of data scarcity, one of the main limitations of current urban scale building energy simulation models, is the use of deterministic approaches to specify modelling inputs for entire classes of buildings. This modelling approach is characterised by three major shortcomings: first, data uncertainties are not comprehensively considered; second, a rigorous method of identification of groups of buildings to be populated by similar modelling parameters is not specified; and third, strategies to calibrate the developed energy models are missing. Considering these challenges, the current paper presents a non-deterministic calibration method for groups of buildings. The methodology utilises four techniques: (i) the use of clustering to identify building groups and associated representative buildings within the urban context; (ii) the application of an automatic building energy modelling approach to simulate buildings within these groups; (iii) the application of data-driven models, used as emulators of the dynamic simulation engine, in a computationally efficient manner; and (iv) the exploitation of a non-deterministic Bayesian calibration framework to identify sets of representative parameters for the building clusters. Datasets, based on 2646 buildings from the city of Geneva, Switzerland, are used as a case study. Validation with measured data, shows that energy consumption and energy intensity predictions when considered at an aggregated scale, are within +/– 5%. On an individual building basis, the validation error is within +/– 20% for approximately 70% of the buildings. [ABSTRACT FROM AUTHOR]

• Telework impacts transportation, building, and Internet-related energy and emissions. • Telework's environmental impact is highly dependent on contextual details. • The majority of studies indicate telework decreases energy use, with some exceptions. • Rebound effects tend to significantly offset and even exceed energy savings. • Existing research methods tend to be simple, leaving room for significant improvement. Teleworking has been widely perceived as a more sustainable mode of working for knowledge workers compared to the status quo of commuting to centralized offices because of its reduced dependency on transportation and centralized office space. However, the situation is far more complex than would appear on the surface, when the scope is expanded to include home office energy use, the Internet, long-term consumer choices, and other so-called rebound effects. Few studies have quantified home, office, transportation, and communications energy or GHG emissions implications of telecommuting simultaneously. To make progress in answering the question of whether telecommuting results in less energy use and greenhouse gas emissions than conventional centralized office working, this paper reviews results and research methods of primarily quantitative studies of any and all four domains that consider operating energy and/or greenhouse gas emissions. The results ultimately show that this problem is complex, and that current datasets and methods are generally inadequate for fully answering the research question. While most studies indicate some benefit, several suggest teleworking increases energy use – even for the domain that is thought to benefit most: transportation. [ABSTRACT FROM AUTHOR]

• The environmental analysis of different insulation materials is presented. • The operational phase is the most critical stage during the whole life cycle. • The considered mineral wool showed the best environmental impact. • The studied PU, MW, XPS showed an encouraging environmental payback. The construction industry is one of the less sustainable activities on the planet, constituting 40% of the total energy demand and approximately 44% of the total material use and the generation of 40–50% of the global output of greenhouse gases. The biggest environmental impact caused by buildings is generated during their operational phase due to the energy consumption for thermal conditioning. Hence, in order to reduce this energy consumption, insulation materials must be used and from a life-cycle perspective, the use of insulation materials reduces the building impact over time. This paper develops a comparative life cycle assessment (LCA) of different insulation materials (polyurethane, extruded polystyrene, and mineral wool) to analyse the environmental profile of each insulation material type in the Mediterranean continental climate. Significantly, all three insulation materials demonstrated a net positive benefit over a fifty-year life span due to the reduced heating requirements of the building. Results showed that the highest environmental impact was associated with the polystyrene insulation material and the best environmental performance was for the mineral wool. Moreover, regarding the consumption, polyurethane and mineral wool had similar thermal performance during the whole year. Furthermore, the environmental payback period shows that the cubicles with insulation material are environmentally efficient, if they are used for at least 7 years (for mineral wool), 10 years (polyurethane), and 12 years (extruded polystyrene). The results of this research give new insights into the effect on building insulation materials. [ABSTRACT FROM AUTHOR]

• The review focuses on energy prediction techniques for large-scale buildings. • Five types of energy demand determinants in large-scale buildings were analyzed. • The techniques are classified the black, white, and grey-box based approaches. • A variety of energy-related applications, including energy-mapping were analyzed. • The hybrid models are suitable for energy predictions in large-scale buildings. Building energy prediction techniques are the primary tool for moving towards sustainable built environments. Energy prediction models play irreplaceable roles in making energy policy and the development of the building sector. This paper presents a comprehensive review of the prevailing prediction techniques used in large-scale building energy applications under different scopes and different archetypes, including black-box, white-box, and grey-box based methods. Additionally, the advantages and disadvantages of the applications of those approaches are compared and discussed in the context of large-scale buildings. The review results show that prediction techniques have addressed a variety of large-scale building energy related-applications, such as energy consumption forecasting and prediction, energy consumption profiling, energy mapping and benchmarking of buildings. However, there are still some research gaps that require more attention such as the inclusion of occupant behavior in white-box based models and the explicit representation of end-uses in black-box based models. Significantly, this review concludes with a few key tasks for modification of the current prediction approach framework, which can help with forecasting future energy use changes of specific buildings during the retrofit process or inclusion of renewable energy technology. This would assist in developing an appropriate strategy for the sustainability of the built environment. [ABSTRACT FROM AUTHOR]

• Urban form factors that affect heating load in severe cold regions are discovered. • Heating load can be saved by 6.76% with every unit increased in surrounding FAR. • Heating load can be saved by 12.76% with every unit increased in surrounding RHR. • How urban morphology influence heating load in severe cold regions is discussed. • Urban design strategies are provided for reducing heating load in severe cold regions. Studies have suggested that the energy demand of buildings can be significantly influenced by urban morphological factors. However, little research has focused on urban-morphology-related heating energy consumption in severe cold regions, where buildings are facing greater pressure to save energy because of long heating periods. In this paper, the aim is to fill this knowledge gap through a combination of energy simulation and statistical analysis. First, taking 73 office buildings in Harbin as the study objects, the theoretical energy consumption (TEC) is stimulated and the external influence energy consumption (EIEC) of each building is obtained by subtracting the TEC from the measured actual energy consumption. Then, using regression analysis, the effects of 7 urban morphological factors on heating energy consumption are evaluated. The results indicate that greater building site cover (BSC), floor area ratio (FAR), building height (BH), road height-width ratio (RHR), total wall surface area (WSA), and lower green space ratio (GSR) are beneficial to the reduction of heating energy consumption. Among them, FAR is the most critical factors in saving heating energy by up to 10.820 kW·h/m2/y, with the addition of one more unit. This result confirms the significant role that energy saving-oriented urban morphology can play in making heating energy more efficient and sustainable in severe cold region cities. [ABSTRACT FROM AUTHOR]

• Use of GFRG building technology promotes the concept of industrial symbiosis. • Reduction in initial embodied energy due to GFRG building technology is determined. • Benefits of GFRG technology include savings on materials, time and manpower needs. • Significant reduction in self-weight of GFRG buildings and its related advantages. The present situation vis-á-vis increased energy use and environmental emissions has urged the building sector to contribute more towards sustainable construction. This is possible only through appropriate choice of building materials and technologies that minimize the environmental impacts. Nevertheless, the adoption of the same need to necessarily satisfy the user in terms of aesthetics, utility and cost. This paper presents the comparison of embodied energy of a two-storey residential building, made using, i) GFRG (glass fibre reinforced gypsum), and, ii) load-bearing brick masonry, building technologies. GFRG technology uses load-bearing walls and slabs that are manufactured using gypsum waste, and consumes reduced quantity of reinforced concrete in comparison with conventional construction. It has been found in this study that the embodied energy related to building materials of the GFRG building is 16% less, and that associated with transport of resources is twice, compared to the conventional brick masonry building. The major advantages of the GFRG building construction are found to be about – reduction in structural weight and construction cost, – savings in construction time and – savings in total man power requirement for construction. Further, the use of cement, steel rebar and sand is reduced by 25%, 36% and 44% respectively. [ABSTRACT FROM AUTHOR]

• We selected three benchmarking methods to establish building energy benchmarking models based on a sample of 45 hotel buildings in China. • We compared the three benchmarking methods in terms of consistency, robustness and explanatory ability. • The energy efficiency performance obtained from the three methods were found to be inconsistent. • We came up with suggestions based on comparative analysis for policy-makers to develop building energy benchmarking programs. A reasonable building energy efficiency benchmarking program plays an important role in energy consumption control and supervision. Previous studies have focused on the process of establishing a single benchmarking method, but few have compared the performances and outcomes of different methods. To fill this gap, this paper selects three benchmarking methods—multiple linear regression (MLR) based on Energy Star, stochastic frontier analysis (SFA) and the descriptive statistics method (DSM) based on the national energy consumption standard in China—to develop benchmarking models. We demonstrate each method using data on the energy and building characteristics of 45 four- and five-star hotel buildings located in Chongqing, China. To compare the consistency, robustness and explanatory ability of the three methods, we first utilize the Spearman rank correlation analysis to test whether these methods have consistent energy efficiency ranks and then present Sankey diagrams to further reveal the interactions of the estimated energy efficiency grades obtained from the three methods. It is found that the results of DSM and SFA are most consistent, while MLR vs. SFA and MLR vs. DSM present significant differences in evaluating building energy performance. In addition, DSM is more robust for evaluating the ranks of sampled buildings, while SFA is more robust for evaluating energy efficiency grades. Furthermore, we discuss the explanatory ability of each method. In addition to the building characteristics, the design and operational characteristics of the HVAC system have great effects on building energy consumption. Finally, we present suggestions for policy-makers regarding the development and implementation of the building energy benchmarking program in Chongqing and for the management of buildings with different energy performances to further improve the energy efficiency. [ABSTRACT FROM AUTHOR]

The South Korean government is presently conducting a research project to provide measurement-based building energy consumption data for individual end uses, such as space heating, space cooling, domestic hot water (DHW), and other uses. From 2014 to 2019, energy consumption measurement systems for each end use were installed in sample buildings, and the measured data are being statistically processed with the objective of constructing an information system for energy use and CO 2 emission intensities by end use. This paper presents the statistical data for the site and primary energy use intensities (EUIs) and the CO 2 emission intensities by end use for the period of May 2017–April 2018; the data were obtained from measurements in 71 sample apartment units located in Seoul. The average site EUIs display the following order: space heating > electrical appliances > DHW > cooking > lighting > space cooling > air movement. Electrical appliances have the largest average primary EUI and CO 2 emission intensity. The average site EUIs exhibit the same order as those of the US Residential Energy Consumption Survey 2015 data: space heating > electrical appliances, etc. > DHW > space cooling. [ABSTRACT FROM AUTHOR]

• Evaluation of existing approaches or projects of building archetype. • Developing a generalized multi-scale building archetype methodology. • Implementing the data-driven approaches for the characterization of building archetypes. • Comparison of the modeling results of multi-scale building archetype at BEM and UBEM levels. Globally the building sector accounts for a significant portion of the overall energy demand and greenhouse gas emissions of any country. The most common approach for the collection of modeling and benchmarking data that can be used for predictions of energy performance at a national or urban scale is through classification of the building stock into representative archetypes. Developing such building archetypes is a complex task due to the difficulties associated with gathering detailed geometric and non-geometric data at an urban scale. Although existing databases and projects provide a valuable overview of a building stock, the information about buildings' physical descriptions are not regularly updated. Moreover, these databases cover only the national top-level archetypes and lack crucial information related to city or district scale building stocks. The use of national scale archetypes requires many assumptions that may not hold true for energy modeling at urban or district scale. This paper proposes a multi-scale (national, city, county and district) archetype development methodology using different data-driven approaches. The methodology consists of following five steps: 1) data collection, 2) segmentation, 3) characterization, 4) quantification, and 5) modeling results. We developed a test case based on the available building stock data of Ireland. The test case used previously developed archetype geometries coupled with the parameters determined by the characterization process to calculate annual energy use (kWh) of buildings at a multiple-scales. The resulting archetypes at national, city, county and district scale are analyzed and compared against one another. The results indicate that significant differences occur in terms of energy modeling results when national scale archetypes are used to simulate the energy performance of buildings at the local scale. These multi-scale building archetypes will aid local authorities and city planners when analyzing energy efficiency and consequently, help to improve sustainable energy policy decisions. [ABSTRACT FROM AUTHOR]

• Uncertainty is a significant barrier to low-carbon investments in real estate. • The model simplifies the management of energy refurbishment in building portfolios. • Linear optimization is integrated with a 2D-Monte Carlo analysis to deal with risk. • Energy retrofit is seen as an optimization problem with objectives and constraints. • Tax incentives, energy price and bank spread represent the critical variables. In recent years, an increasing interest has been addressed to the energy efficiency of buildings. However, the focus has always been on individual properties, while both literature and practice still lack detailed solutions for entire stocks. Conversely, energy efficiency approaches should consider building portfolios as a whole, given that the deep decarbonisation of existing stocks would bring significant environmental and economic benefits. In this research paper, a decision support model has been developed for planning and managing energy retrofit operations in wide building portfolios. The core idea is to go beyond the single-building perspective and identify the level of energy retrofit that leads to the greatest possible benefit, comprising economic, environmental, human, and conservation targets. Energy improvement is here handled as an optimisation process, showing conflicting objectives and constraints. To this end, several techniques have been combined from various disciplines such as statistics, economics, energy modelling, operational research, and risk analysis, to develop a comprehensive and interdisciplinary methodology. Among the results of this research, the extreme flexibility in comparing countless design scenarios, the limited amount of buildings' characteristics required and the straightforwardness of the model developed are the most important contributions achieved. [ABSTRACT FROM AUTHOR]