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• A novel agent-based model (ABM) was developed to explore occupant behaviors in the context of built environment. • Relevant data was collected using customized sensor nodes and paper-based survey in a test bed building. • A validation study was conducted to test the occupant behavior model with the methods of visualization and calculation of quantification metrics for performance evaluation. • The proposed research framework has the potential to improve both building energy efficiency and occupant comfort during the life cycle of a building. Occupant behaviors are one of the dominant factors that influence building energy use. Traditional building energy modeling programs use typical occupant schedules that often do not reflect actual situations. Robust occupant behavior modeling that seamlessly integrates with building energy models will not only improve simulation performance, but also provide a deeper understanding of occupant behaviors in buildings. This paper presents a development and validation approach to a novel occupant behavior model in commercial buildings. A robust agent-based modeling (ABM) tool, namely Performance Moderator Functions server (PMFserv), is used as the basis of the occupant behavior model. The ABM considers various occupant perceptions and interactions with window, door, and window-blinds based on the environmental conditions. An elaborate agent-based model that represents an office space in an existing building is developed. This is followed by a validation study of the ABM through the use of embedded sensors that capture the indoor ambient conditions and a survey to record actual occupant behaviors. By comparing the recorded behavior data with ABM output, this paper discusses the proposed ABM's prediction ability, limitations, and extensibility. Finally, the paper concludes with the potential of integrating the occupant behavior model with building energy simulation programs. [ABSTRACT FROM AUTHOR]

Increasing use of volatile renewable energy sources causes challenges in balancing supply and demand. Therefore, demand-side flexibility has rising importance for system operators and balancing authorities. Flexibility management methods are needed to integrate loads like ventilation systems of different buildings (e.g., residential and commercial) into flexibility service. However, the available methods described in research papers require further development for implementation in practice. Heating and cooling systems have received much attention from researchers, but the potential of ventilation systems has been left out of focus. Therefore, this paper provides a complete set of novel flexibility management methods for ventilation systems created from an aggregator's viewpoint. The flexibility is quantified through capacity (e.i. the amount of power consumption that can be altered), forced ventilation rate duration, and the tendered price for the service. The proposed methods were tested on a building model constructed and simulated in IDA ICE. The data processing and flexibility management methods were applied in MATLAB. Two types of ventilation systems with different sensor configurations were considered: constant and variable air volume. Forced ventilation rate duration is calculated using energy and mass balance analysis where the root means squared error was 10 to 33 min, depending on the system type, measured parameter, and sensor location. The flexibility service pricing model was tested on the 2022 years' manual frequency restoration reserve (mFRR) activation and balance energy market data. [ABSTRACT FROM AUTHOR]

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]

As most promoted solution of climate change, green buildings become more popular and widely promoted in China recently. This paper compared the carbon emissions of green buildings with non-green buildings; therefore, a life cycle carbon emission assessment approach (LCA) was attempted. In the present paper, we have integrated existing database Building Environmental Load Evaluation System (BELES) developed by Tsinghua University and twenty-six real existing buildings (nine residential and seventeen commercial buildings). Buildings located in two major climate zones were selected, namely, zone II and III. Five phases were defined for a whole life cycle of building: Phase 1 Raw Materials, Phase 2 Transportation, Phase 3 Construction, Phase 4 Operation & Maintenance and Phase 5 Demolishment. In each phase, the emissions were calculated separately. It is found that whole life cycle CO 2 (LCCO 2 ) of green buildings are lower than that of non-green buildings, i.e., 10% for residential and 32% for commercial buildings, respectively. For both residential and commercial buildings, Operational & Maintenance phase contributes the majority of emissions in the whole life cycle, about 69.2–89.3%. Moreover, green buildings have slightly higher embodied CO 2 emissions than non-green buildings, but with much lower operational emissions, and this phenomenon is significant in residential buildings. This indicates that the LCCO 2 of green buildings start to match with LCCO 2 of non-green buildings after operating for certain years, which is the turning point. Furthermore, China implemented Standard for Energy Consumption of Buildings in December 2016, which listed two values for energy efficiency in operational phase, which represents requested and optional. It can be expected that the emissions in Operational & Maintenance phase of green buildings will be further reduced in the future. Consequently, in the future, the dynamic flow of turning point of residential buildings will be shifted downwards from existing 14 years found in this paper. [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]

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

Summer peak electric demand of commercial buildings and districts is typically driven by the cooling load. This paper presents a novel approach to holistically optimize both the air- and water-side of the HVAC system in existing commercial districts. Therefore, this paper is different from the existing literature in its focus on integrated optimization of both air- and water-side of the HVAC system in a district scale as opposed to only airside or waterside. Methodologies proposed in this paper for integrated optimization of air- and water-side are brand-agnostic and their capabilities are demonstrated using a chilled water plant at Colorado School of Mines' campus. Based on the results for the test district, Mines' integrated air- and water-side optimizations of the HVAC system reduces its peak electric demand by 15% and 13%, respectively, for August 19 and 20, 2019. The air- and water-side optimization of the HVAC system resulted in greater savings than that achieved with only the waterside demand optimization method discussed in authors' previous paper focused on existing commercial districts. [ABSTRACT FROM AUTHOR]

Traditional prediction methods of building cooling load fail to consider load changes caused by the time factor, which results in the failure of the best predicting effect and portability. To explore a more accurate cooling load predicting approach, in this paper, a new hybrid model based on decoupled weight decay Adam optimized improved LSTM neural network, denoted as DwdAdam-ILSTM, is proposed. First, the cross-correlation function is used to extract features with a high degree as the input data. Then, the decoupling weight decay adaptive moment estimation (DwdAdam) optimization algorithm is utilized to optimize the parameters of the improved long short term memory (ILSTM) prediction model. Next, the ILSTM is employed to establish a prediction model. Finally, to verify the validity and prediction accuracy of the proposed model, this paper selects the hourly cooling load data of a large commercial building in Xi'an as the research sample. Results of the experiment show that the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), Variation Coefficient of Root Mean Square Error (CV-RMSE) and Range of Relative Errors (E-R) of DwdAdam-ILSTM predicting the cooling load are 2.2%, 2.74, 5.2%, 0.061, 0.216. Compared with three version variant LSTM neural networks, the MSE and E-R values of this model are decreased by 74%-53%, 42%-49%, and 69%-64%, which has better performance in accuracy and stability. It testifies that the proposed strategy is reliable and effective. Additionally, this model can provide important guidance for daily energy management and multi-energy scheduling of buildings, which is helpful to the application of cooling load prediction. [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]

• Thermal performance of energy pile systems was evaluated by performing field tests and numerical simulations. • The effect of thermal interference on the thermal behavior of energy piles was investigated. • A design chart for considering the thermal interference effect depending on the denseness of heat exchange pipes was provided. In general, the design of closed-loop vertical ground heat exchangers (GHEXs) is to determine the required borehole length for providing the heating and cooling load of a target building. However, the same design procedure cannot be applied to the cast-in-place energy pile since the configuration of the heat exchange pipe inside the pile is too varied and the size of the borehole is relatively large. In this paper, the effect of configuration and denseness of heat exchange pipe inside the energy pile was experimentally and numerically evaluated comparing with conventional GHEXs. First, field experiments were performed for six cast-in-place energy piles constructed in a test bed. The results showed that the cast-in-place energy piles could provide higher thermal performance per unit borehole length than the other conventional types of GHEXs by increasing the heat exchange area along with much longer heat exchange pipes. However, the over-tight layout diminished the improvement of thermal performance due to thermal interference between each pipe loop. In addition, the long-term thermal behavior of energy piles and changes in ground temperature were estimated with numerical simulations. With the simulation results, a design chart was provided for evaluating the effect of thermal interference on energy pile performance. [ABSTRACT FROM AUTHOR]

• Thermal, luminous and energy efficiency of single-glass with solar films is assessed. • Experimental data was used to calibrate a simulation model in EnergyPlus program. • Films with internal and external application on the side of the glass were analyzed. • Cooling, heating and lighting energy savings assessed for several solar orientations. • A decision-making framework based on performance criteria is provided. This paper examines the thermal and luminous performance of single-glazed windows with and without solar control films (SCFs) and a venetian blind in actual working conditions. SCF is a passive solution to modify the solar-optical properties of glazing systems, in order to reduce the solar gains and the energy use and to increase the indoor comfort conditions. An experimental campaign was carried out simultaneously in both cooling and heating seasons in two similar office rooms, one with a SCF applied on the internal surface of the glass and the other without any SCF. The experimental data was used to calibrate a model in EnergyPlus and to assess the energy performance of several SCFs with different thermal and optical characteristics for different orientations of the façade. A decision-making framework was applied to identify the potential use of SFCs as retrofitting solutions for single-glazed windows based on energy performance criteria. The results show that SCFs have a real impact in reducing the cooling energy use for South, East and West solar orientations (SCFs with low solar transmittance coefficients show reductions of the cooling energy use up to 86%) due to the reduction of solar gains, while there is an increase in the heating and lighting energy use. [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]

[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]

Non-intrusive load monitoring (NILM) has become an emerging technology in the energy sector for its effectiveness in the energy disaggregation of individual loads from the measured aggregated energy in main power supply of building. NILM has also evolved gradually over the past few decades, due to the significant advancements in artificial intelligence (AI), embedded/edge devices, and internet of things (IoT). From the review of literature, we observed that most of the NILM systems were only capable of monitoring different loads and it fails to detect and disaggregate the energy consumption of similar loads effectively. Further, most of works in the literature were focused on improving the performance of energy disaggregation of different loads in residential buildings and very limited works have been carried out for the similar loads in NILM system. Therefore, there is a necessity to study and evaluate the state of the art NILM algorithms for similar loads in real case scenarios for effective implementation in the commercial and industrial buildings. In this paper, we present a case study of the developed DeepEdge-NILM device which has been installed in a commercial building (office environment), where multiple similar and identical air conditioners (AC's) are used in the monitoring environment. A deep learning framework, Long Short-Term Memory (LSTM) has been implemented on the new set of features which comprises of electrical features and intrinsic features derived from the basic electric features using geometric mean for capturing the signature of similar loads effectively. From our case study, we observed that the performance of the deployed NILM system outperforms the baseline systems that have been developed using basic electrical features significantly. Finally, the limitations and future scope of the NILM edge device in the commercial buildings are also discussed. [ABSTRACT FROM AUTHOR]

[Display omitted] • Organic PCM shows a appropriate phase change point considering the ambient temperature. • Shape stabilisation is the most prevailing incorporation because friendly and cheap operation. • Physical properties of PCM composites should get attention as equivalent to thermal properties. • The performance of PCM relies heavily on the ambient condition of construction. Building construction deserves many attentions due to its huge energy consumption, while Phase Change Materials (PCMs) provide positive solutions for improving energy efficiency and enhancing the thermal properties of construction materials. However, PCMs also present some negative impacts, such as weakening mechanical properties and increasing costs, chemical instability and so on. In this paper, the main characteristics of PCMs, design and incorporating methods, effects on energy consumption and construction reliability are comprehensively reviewed and discussed. Although many materials have the capacity of phase change, some organic PCMs are more suitable due to the higher latent heat and favourable phase change point in buildings, when eutectic PCMs present greater potential to become the optimal one but much effort is required for investigations. Current design methods and application in construction materials can meet the essential requirements, but the effectiveness is inadequate, including low efficiency of phase changing, leading to low energy storage. Subsequently, some promising research direction and critical areas for optimization are also proposed accordingly in this paper. Future development of PCMs, including novel PCM and efficient incorporation, real applications and functions in buildings are proposed. Additionally, multifunctional construction materials combining PCM deserve much attention and possess promising prospect for energy saving in sustainable and energy efficient building construction. [ABSTRACT FROM AUTHOR]

Faults in air handling units (AHUs) in commercial buildings often waste energy and/or cause discomfort. Intermediate AHU sensors are dedicated merely for diagnostics and are essential to isolate faults rather than for controls; However, in many AHUs, such sensors are either missing, misplaced, or uncalibrated. This paper investigates three model-based methods to make up for the lack of reliable intermediate sensor data. To this end, trend data from five AHUs in five different buildings in Ottawa, Canada, are extracted for 2019. The accuracy of three different model forms is compared - artificial neural network (ANN), genetic algorithm (GA), and multiple linear regression (MLR) - are applied to model the supply air temperature of the AHUs. The behaviour of AHU heating and cooling coil valves and outside air dampers with and without the intermediate sensors is studied in this paper. Although installing temperature sensors before and after the heating and cooling coils facilitates detection of the faults occurring in AHUs, the authors showed the generated inverse models can act as virtual temperature sensors to estimate the intermediate measurements and isolate hard faults in AHU outside air dampers in addition to heating/cooling coil valves. [ABSTRACT FROM AUTHOR]

The occupant windows opening behavior has a great impact on indoor air quality and building energy consumption. Therefore, measuring the window opening behavior and factors that affect it are important for the occupant behavior modeling and architectural design. In this study, we proposed non-intrusive measurement methods which can achieve large-scale sampling for the window state. An image recognition code based on MATLAB was used to conduct projective transform of the building elevation maps, identify the window positions and determine their opening proportions. The method can recognize most of window open states with the error of 8%. Based on this method, the window opening states for a hospital building from August to December in 2018 (about 6000 samples) was collected. Then the significance of the influencing factors and window opening distributions under different factors were analyzed. The results showed the outdoor temperature had the most significant effect, and the frequency of the window opening proportion in the range of 0.3–0.5 within 20–30°C is significantly higher than other temperature ranges. This large-scale sampling method proposed in this paper provided a powerful tool for building modeling and energy analysis. [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]

The energy demand for supplying Domestic Hot Water (DHW) has an important share in the overall thermal energy consumption of commercial buildings. The aim of this paper is to analyze the performance of DHW production in commercial public service, i.e., educational buildings, to subsequently identify frequent inherent shortcomings of centralised DHW installations and to provide measures for optimization or economically more viable solutions. Several buildings of the University of Natural Resources and Life Sciences in Vienna have been investigated in terms of DHW consumption and performance of the installed centralised systems. Referring to the investigated university buildings, it can be stated, that in cases of low to medium consumption figures, typical centralized DHW installations with a hot water circulation pipework achieve only poor efficiencies in the range of 2–12%. As for one particular centralized DHW system in operation, focusing on demand controlled DHW supply through adapted flow control, e.g., reducing the runtime of the circulating pumps at specific time intervals, and, if applicable and feasible, decreasing hot water flow- and storage capacities can reduce annual energy consumption for DHW by 15–25% and improve the overall system efficiency significantly as could be evaluated in a detailed case study. A seemingly economical yet ecologically controversial option for improving centralised DHW systems is the partial or complete conversion to electric point-of-use water heaters. This reduces thermal energy losses almost completely, hereby significantly increasing the efficiency of a DHW supply system. Nevertheless, existing hot water supply systems, energized by district heating, usually consume less primary energy and most likely generate fewer CO 2 emissions compared to electric DHW heaters. [ABSTRACT FROM AUTHOR]

• Propose a methodology to create prototypical building energy models for existing buildings. • Develop prototypical building energy models for existing US religious worship buildings. • Provide a case of applications to guide the users to use the models. Prototypical building energy models are of great significance because they are the starting point in conducting analyses for various applications, such as building energy saving potential analysis, building design, building energy market evaluation, and building energy policy-making. However, current prototypical building energy models only represent limited types of buildings in certain countries. To fill the gap, this paper proposes a methodology to systematically create prototypical building energy models. First, a six-step methodology is introduced: model input identification, data collection, data cleaning, data conversion, model simulation, and model calibration. Then, the methodology is demonstrated by a case study of creating 30 prototypical energy models for U.S. religious worship buildings, representing buildings in 15 climate zones and 2 vintages (pre- and post-1980). Finally, to show the applications of the models, the building energy saving potentials from six efficiency measures are analyzed for pre-1980 U.S. religious worship buildings in three ASHRAE Climate Zones. The results show that the maximum energy saving potentials are approximately 30% for the religious worship buildings in all three climate zones investigated, indicating significant opportunities for energy savings in these buildings through their prototypical building model development. [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]

The energy use prediction of building systems is crucial to design a high-efficiency building and maintain low energy consumption operation, which is also important in optimizing building system control and retrofitting. This paper demonstrates a comparative study of four data-driven methods used in online building energy predictions involving large-scale data extracted from several types of buildings. The characteristics of building electricity use and data reliability were addressed through the data pre-treatment process including visualization, cleaning, parsing and filtering. Mathematical algorithms and their applications in previous studies were summarized and compared, and evaluation methods were developed. The performance and suitable application scenarios of the proposed algorithms were conducted via the comparison of monitoring data and predicted results. The study indicates that the most complex method which requires the highest computation ability, i.e., the Artificial Neural Network (ANN), does not lead to the highest accuracy, while as the fastest computation method, Gaussian Process Regression (GPR) usually has the results with the lowest accuracy. Support Vector Machine (SVM) and Multivariate Linear Regression (MLR) methods usually perform better in the case scenarios studied. All the prediction accuracies can meet the requirements of RMSE <30% and NMBE <10% proposed by ASHRAE, and the computation time varies from less than 1 s to 22 s per prediction. All these methods/algorithms worked well for buildings with stable energy use patterns. For buildings with complex and unstable occupancy schedules and energy use patterns, MLR and SVM methods have the ability to achieve a high accuracy with fast computation speed. [ABSTRACT FROM AUTHOR]

Highlights • This article describes a regression approach to model emerging HVAC technologies. • The process is detailed using a Vuilleumier-cycle (VC) heat pump as a use case. • Results show annual energy and cost estimates for a VC heat pump in several climates. • A VC heat pump uses 40% less source energy for heating than a conventional furnace. • A VC heat pump uses more source energy than a standard-efficiency air conditioner. Abstract This paper presents a regression-based approach for modeling emerging heating, ventilation, and air-conditioning (HVAC) technologies using the user-defined coil object in the building simulation software EnergyPlus. The approach can use output from HVAC system models or experimental data to create regression-based performance curves, which are then used within the building simulation. The regression approach is an alternative to modeling new HVAC technologies directly in EnergyPlus or performing co-simulation with a system model in a separate program. The proposed approach connects component and system level designs, and their associated performance, with annual energy use and costs in a variety of climates. This study focused on a Vuilleumier-cycle heat pump for a commercial building. We describe (1) the design and modeling of this system, (2) methods for translating this performance into a simplified model in EnergyPlus, and (3) building energy simulation results in United States climate zones 4–6. The results predict that the Vuilleumier-cycle heat pump will use 34 to 44% less source heating energy than an 80% efficient gas furnace, and more source cooling energy than a SEER 13 air conditioner. Total energy costs are lower when electricity prices are high and natural gas prices are low. [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]

Cooling demands of commercial buildings present a relevant challenge for a sustainable future. They account for over half of the overall energy needs for the operation of an average office building in warm climates, and this situation is expected to become more pressing due to increasing temperatures in cities worldwide. To tackle this issue, it is widely agreed that the application of passive strategies should be the first step in the design of energy efficient buildings, only using active equipment if it is truly necessary. Nonetheless, there is still further need for information regarding the potential limits derived from their application. This paper explores the effectiveness of selected passive cooling strategies in commercial buildings from warm climates, defining performance ranges based on the assessment of multiple scenarios and climate contexts. This task was conducted through the statistical analysis of results from documented research experiences, to define overall ranges and boundary conditions; and through software simulation of selected parameters to isolate their impact under a controlled experimental setup. General findings showed that the mere application of passive strategies is not enough to guarantee relevant savings. Their effectiveness was conditioned to both the harshness of a given climate and different building parameters. Specific recommendations were also discussed for the selected passive strategies considered in the evaluation. [ABSTRACT FROM AUTHOR]

• We review sixteen models for Variable Refrigerant Flow System (VRF). • We develop three data-driven models for VRF. • Physics-based and data-driven models are compared on a real-world dataset. • The impacts of different input features on VRF efficiency are analyzed. Variable Refrigerant Flow (VRF) systems are gradually gaining popularity in small and medium-sized commercial and residential buildings owing to their high part-load performance, flexible control, and ease of installation and maintenance. Developing models of VRF systems to predict their performance are important for model-based control, fault diagnostic and detection. There are VRF models published in existing literatures, however those models were developed and validated in different datasets. As a result, the model accuracy cannot be directly compared. To fill this gap, this paper presents a comprehensive review of the existing VRF models, and summarizes the input/output parameters and mathematical formulas of 16 VRF models from literature (referred to as physics-based model). Next, we validate and compare the model accuracy of existing models using the same dataset. Additionally, we develop data-driven models using the state-of-art machine learning algorithms, and compare the model accuracy between existing physics-based models with data-driven models. We find the model proposed by Hu et al. in 2019, which regresses the VRF cooling capacity and COP as a linear combination of indoor and outdoor temperatures times a cubed polynomial function of compressor frequency, is the most accurate physics-based model, with a prediction error of 22.19% in the training dataset and 22.44% in the validation dataset. XGBoost is the most accurate data-driven model, with a prediction error of 19.29% in the training dataset and 22.02% in the validation dataset. The data-driven model is more accurate while the physics-based model is more generalizable. The findings of this study can help researchers to select the proper VRF model for building energy prediction, model-based optimization, and fault diagnostic and detection. [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]

• A coupled scheme to integrate urban building energy model and physical-based BIPV potential estimation approach. • Demonstrate carbon neutrality assessment workflow for commercial building stock at multiple scales. • Spatiotemporal assessment of energy use intensity, reduction potential and peak loads of commercial building stock. • 84% reduction potential with the simultaneous implementation of all the measures. • Decarbonization potential of BIPV is less than efficiency measures in commercial building stock. The transitional pathway to carbon neutrality in commercial building stock requires improvement of energy efficiency, use of renewable sources and implementation of non-technological mitigation strategies. However, several methods have been developed to assess the carbon neutrality of commercial building stock but they mainly lack the ability of spatiotemporal quantification of energy demand and decarbonization potential which hinders the evaluation of carbon neutrality at multiple scales. This paper presents a GIS-synthetic hybrid urban building energy model (UBEM) coupled with a physical-based approach of building integrated photovoltaics (BIPV) potential estimation to incorporate a building-level energy model at a large scale that could further evaluate the feasibility of carbon neutrality of commercial building stock at a multi-scale level. As a case study, the proposed coupled scheme is applied to the commercial building stock of Tokyo to evaluate the cofound influence of active and passive design measures, and BIPV on the overall decarbonization potential. The results indicate the following findings: the annual CO 2 emissions can be reduced by 84% with the simultaneous implementation of all the measures; focusing on passive design measures is not enough to achieve carbon neutrality in commercial buildings that only contribute 10% of the overall reduction potential; 16% to 40% of the decarbonization potential is contributed by BIPV that is less than efficiency measures; and the scale-bounded comparative analysis shows that total BIPV potential changes in the order of magnitude of 29 to 54 with the variation of scale (with reference to district scale) and 4–16% more self-sufficiency observed at the building-by-building level in comparison to district and city scales. This demonstrates that this coupled approach can provide a purpose-driven perspective for the energy transition of commercial building stock at multiple scales with reduced computational time. Overall, this analysis provides a multi-level perspective to energy modelers and policymakers on how to achieve carbon neutrality in the commercial building stock. [ABSTRACT FROM AUTHOR]

Occupants’ energy-consuming behaviors have a significant influence on overall energy consumption in commercial buildings. Accordingly, understanding and intervening in these behaviors offers a significant opportunity for energy savings in commercial buildings. Current approaches to behavior modification rely on available occupant-specific energy consumption data, but capturing such data is generally expensive. One possible solution to this challenge is to link energy consumption to individual occupants’ energy-use behaviors in commercial buildings. In this context, this study proposes a non-intrusive occupant load monitoring (NIOLM) approach that couples occupancy-sensing data—captured from existing Wi-Fi infrastructures—with power changes in aggregate building-wide energy data to thereby disaggregate building-wide data down to the individual. This paper describes two case studies that investigate the feasibility of using the NIOLM approach to identify occupant-specific energy consumption information. Tracking eleven occupants’ energy-use behaviors using NIOLM over a four-month period resulted in an average F-measure of 0.778 and Accuracy of 0.955. The case studies thereby demonstrated that NIOLM successfully tracks individual occupants’ energy-consuming behaviors at minimal cost by utilizing existing high-resolution metering devices and Wi-Fi network infrastructures in commercial buildings. [ABSTRACT FROM AUTHOR]

In the design of low-energy buildings, mathematical optimisation has proven to be a powerful tool for minimising energy consumption. Simulation-based optimisation methods are widely employed due to the nonlinear thermal behaviour of buildings. However, finding high-quality solutions with reasonable computational cost remains a significant challenge in the building industry. In this paper, Ant Colony Optimisation for continuous domain (ACOR) is developed and applied to optimise a commercial building in Australia. The results for a typical commercial building showed that optimisation can achieve an additional energy savings of more than 11.4%, even after some common energy saving measures were implemented (e.g. double pane windows). The performance of ACOR was compared to three benchmark optimisation algorithms: Nelder-Mead (NM) algorithm, Particle Swarm Optimisation with Inertia Weight (PSOIW) and the hybrid Particle Swarm Optimisation and Hooke-Jeeves (PSO-HJ). This comparison showed that ACOR was able to consistently find better solutions in less time than the benchmark algorithms. The findings demonstrate that ACOR can further facilitate the design of low-energy buildings. [ABSTRACT FROM AUTHOR]

Ancillary services are required to balance supply and demand in electric power systems. Demand response may provide attractive options for these services, through means such as varying the power consumption of commercial building HVAC systems. However, experimental results from a 30,000 m 2 office building suggest that when a building provides ancillary services it consumes more energy than when it is operated normally. This translates to additional costs and environmental impacts. This paper investigates potential causes for building inefficiency associated with ancillary services provision. We develop a physics-based simulation model that captures heat exchange processes and fan and air duct dynamics. During an ancillary service event, we vary the fan power consumption, and then compute the difference between the baseline and actual energy consumption to determine the efficiency of the actions. We explore the impact of building parameters, control design, and baseline model accuracy on the efficiency. In simulation, we find that shorter duration power changes and less aggressive controllers result in less change in energy consumption. We also find that baseline error has outsized effects on the efficiency calculations. These results offer new understanding of the mechanisms underlying inefficiency and point to opportunities to reduce associated costs and environmental burdens. [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]

School buildings have a great potential for carbon emission reduction since their annual emission is about 72 million metric tons. Currently, more than 30 % of school buildings were built before 1960 and are underperforming. To effectively reduce carbon emissions via school building retrofits, it is critical for policymakers to understand the carbon intensity reduction potential of retrofitting school buildings in different regions. Hence, this study develops a method to comprehensively assess the long-term carbon intensity reduction potential of aggregated commercial buildings on a county-by-county basis in the continental U.S. We apply this method to the K-12 school buildings including primary and secondary school buildings. This paper predicts the carbon intensity reduction potential of K-12 school buildings with eight building retrofit measures from 2022 to 2050 in the continental U.S. The results reveal several interesting findings: (1) In the approximately 3,000 counties of the U.S. from 2022 to 2050, the carbon intensity reduction potential of retrofitting K-12 school buildings in each county ranges from 0.41 kg/m2 to 40.00 kg/m2. (2) Even in the same climate zone, the trends of carbon intensity reduction potential from 2022 to 2050 are different depending on their electricity sources. For example, in a hot and humid climate zone, the carbon intensity reduction potential in Florida will decrease from 2044 to 2048. However, in Mississippi, the carbon intensity reduction potential from 2044 to 2046 will increase due to the termination of the nuclear energy usage. (3) Generally, reducing lighting power density leads to more carbon intensity reduction in most states, but it might not be applicable for states with high clean energy penetration, such as Washington. [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]

• Traditional envelope leakage tests are labor-intensive and disturb building operation. • An innovative approach to measuring leakage using MEMS-based sensors is proposed. • A lab test compares MEMS-based sensors with traditional differential pressure sensors. • A field test uses the new pressure sensors to measure envelope leakage. • The new approach can estimate envelope leakage within 3–16% of a standard test. Micro-electromechanical systems (MEMS)-based sensors have seen significant improvements in accuracy and cost over the past several years, and due to the need for altitude sensing in unmanned aerial vehicles and wearable technologies, absolute pressure sensors have shown the most dramatic improvements. This paper investigates applications of MEMS sensors in commercial buildings, including pairing two absolute pressure sensors to determine differential pressures between different locations in and around buildings. Differential pressure measurements are commonly made in commercial buildings for a number of applications, including static pressure measurements for variable-speed supply fan control, air balancing in hospitals, and for envelope and duct air leakage testing. This paper focuses specifically on envelope air-leakage testing applications of these sensors. The use of continuous pressure measurements in addition to one-time non-intrusive outdoor airflow measurements of rooftop packaged units is investigated. A comparison with standardized leakage testing indicated that continuous measurements with MEMS pressure-sensors were able to estimate envelope leakage to within 3–16% of the standardized test result. In addition, the magnitude and stability of differential pressure differences that can be resolved with absolute pressure sensors are also investigated in the laboratory and the field. Results show that the sensors can be effectively used for pressures seen in low rise buildings with rooftop units. [ABSTRACT FROM AUTHOR]

• That aim is to quantify the impact of urban context on building cooling loads. • Building energy modeling is used to test 108 urban context configurations. • The configurations combine different context heights, distances, and orientations. • Savings of 26% and 24% were observed for total and peak cooling, respectively. • Implications on cooling system sizing and urban planning practices are discussed. This paper presents a systematic evaluation of the impact of the built urban context on the cooling energy performance of buildings subjected to extreme hot weather conditions. The proposed approach combines building energy modeling with an extensive parametric variation and statistical analysis scheme. It provides a direct quantification of inter-building shading on energy performance, with an emphasis on the cooling demand patterns for buildings of different types and sizes. Results show that the combined effects of urban context height, distance, and orientation can lead to significant reductions in cooling demand, reaching up to 26% for total cooling loads and 24% for peak cooling loads. The observed savings are particularly notable for residential buildings, making them an ideal target for urban development strategies that aim to leverage inter-building shading effects for energy conservation purposes. Moreover, an in-depth analysis of peak loads illustrates how the density and compactness of the urban form can be used as passive design strategies to reduce the load, and hence, the size of air conditioning systems. Finally, the paper explores how to translate the gained knowledge to design guidelines, bridging the gap between theory and practice. [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 objective of this paper is to review the topics related to the optimized operation of intelligent buildings with respect to occupant comfort and energy consumption. To simultaneously optimize energy costs and indoor environmental quality, intelligent buildings should consider several continuously changing inputs including energy exchange processes across the building, sets of indoor and outdoor environmental parameters, energy prices, occupants' presence, preferences, and behavior inside the building. Therefore, a well-structured framework supported by computational intelligence and optimization methods, environmental monitoring, and behavior modeling techniques, as well as comfort, productivity, and behavioral studies, are required to make optimal decisions for the indoor environment. In this paper, the main concepts, challenges, the latest studies, findings, and developments related to the six topics of (1) Occupant comfort conditions; (2) Occupant productivity; (3) Building control; (4) Computational optimization; (5) Occupant behavior modeling; (6) Environmental monitoring and analysis, in offices, commercial and residential buildings are reviewed. Moreover, future directions and challenges related to the optimized operation of intelligent buildings are discussed. [ABSTRACT FROM AUTHOR]

Over the last years there have been frequent changes to buildings’ thermal legislation, to cope with increasing environmental restrictions and a wider consumer awareness. This work has a twofold objective: 1 – provide an overall methodology to evaluate changes in buildings’ thermal regulations; 2 – to assess the economic merit of these regulatory changes, which have a profound economic impact especially on homeowners, tenants and the global urban real estate market, but also on the society through wider environmental impacts. The literature has provided several contributions at the scale of buildings, but few at the scale of cities (or even regions). This paper uses the city of Lisbon (Portugal) as a case study to test the methodology and determine the economic impact of the changes made in the thermal legislation by the Government. The economic assessment was made at 3 levels: financial analysis, economic analysis and social-economic analysis. It was found that the new regulation brings not only environmental benefits but also economic benefits in long term at both individual and social level. It was furthermore concluded that the impact of those benefits are geographically uneven, and old areas of the city with worse energy ratings have better economic indicators. [ABSTRACT FROM AUTHOR]

To implement an energy saving measure, companies must first decide to investigate the options available. Representative survey data shows that almost half of companies in the Irish commercial sector do not take this step. This paper explores the barriers and drivers of this. Two logit models are fit to data to estimate the influence of variables, representing company and building characteristics, on the likelihoods of a company investigating either a fabric upgrade or a behaviour change measure. Companies are more likely to investigate a fabric upgrade that: own the building they operate from, make energy related decisions locally, have more than 10 employees, have had a recent renovation, accept longer paybacks, and apply a case by case approach to budget decisions. Hotels and offices were found to have a higher likelihood of investigating fabric options. Lack of knowledge of building floor area reduced the likelihood of investigation of both fabric upgrade and behavioural options. Much of the previous research is concerned with the final adoption of measures; this analysis adds additional insights by identifying the factors that determine if a company is likely to investigate the options available. [ABSTRACT FROM AUTHOR]

Building entrance doors are a major source of air infiltration and energy loss in commercial buildings. Previous studies have calculated entrance doors air infiltration and energy saving potential of vestibules with the simplified method which is based on pressure factors. However, challenges are still faced in estimating the pressure difference and the resultant infiltration rates across doors as well as validating the used airflow coefficients under different flow conditions. In this paper, an experimental study is used to validate the airflow coefficient for a fully open single door under both infiltration and exfiltration conditions. The study presents four methods for modeling air infiltration across automatic single and vestibule doors for two reference building models: two methods use the pressure factors and the two others are based on airflow simulations. Energy simulations are then conducted using the air infiltration rates obtained from each method. The results revealed that the design methods overestimate the pressure difference across doors, the air infiltration rates as well as the vestibule savings potentials in comparison to the simulation methods. In conclusion, airflow simulations were found to provide more realistic estimates of pressure differences and infiltration rates across entrance doors when compared to the widely-used design methods. [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]

• Implementation and benchmarking of five advanced control methods. • Comparison of black-, gray-, and white-box MPC with RL and approximate MPC. • Introduction of qualitative KPIs to assess the applicability. • Application in a one-year simulation to evaluate adaptability. Advanced building control strategies like model predictive control and reinforcement learning can consider forecasts for weather, occupancy, and energy prices. Combined with system and domain knowledge, this makes them a promising approach to reduce buildings' energy consumption and CO 2 emissions. For this reason, model predictive control and reinforcement learning have recently gained more popularity in the scientific literature. Nevertheless, publications often lack comparability among different control algorithms. The studies in the literature mainly focus on the comparison of an advanced algorithm with a conventional alternative. At the same time, use cases and key performance indicators vary strongly. This paper extensively evaluates six advanced control algorithms based on quantitative and qualitative key performance indicators. The considered control algorithms are a state-of-the-art model-free reinforcement learning algorithm (Soft-Actor-Critic), three model predictive controllers based on white-box, gray-box, and black-box modeling, approximate model predictive control, and a well-designed rule-based controller for fair benchmarking. The controllers are applied to an exemplary multi–input–multi–output building energy system and evaluated using a one-year simulation to cover seasonal effects. The considered building energy system is an office room supplied with heat and cold by an air handling unit and a concrete core activation. We consider the violation of air temperature constraints as thermal discomfort, the yearly energy consumption, and the computational effort as quantitative key performance indicators. Compared to the well-tuned rule-based controller, all advanced controllers decrease thermal discomfort. The black-box model predictive controller achieves the highest energy savings with 8.4 %, followed by the white-box model predictive controller with 7.4 % and the gray-box controller with 7.2 %. The reinforcement learning algorithm reduces energy consumption by 7.1 % and the approximate model predictive controller by 4.8 %. Next to these quantitative key performance indicators, we introduce qualitative criteria like adaptability, interpretability, and required know-how. Furthermore, we discuss the shortcomings and potential improvements of each controller. [ABSTRACT FROM AUTHOR]

The introduction of smart building technology promises many operational and productivity benefits and enables smart grid integration. A significant barrier to deploying smart building applications is mapping the building sensor metadata to the requirements of the smart building applications. In this paper, we have studied the problem of weakly supervised machine learning to accelerate the metadata mapping process, and demonstrated that weak supervision is a promising approach, which to the best of our knowledge, has not been studied in this context. We developed a pattern-based workflow that enables subject matter experts to craft simple rules that process the idiosyncratic text found in building sensor metadata. Our method was validated using three medium-sized commercial office buildings and a public dataset. We worked closely with a team of commercial building subject matter experts to develop and validate our workflow. We compared our approach to active learning and discovered that weak supervision appears to scale better with building size. Subject matter experts overwhelmingly preferred this approach compared to manual methods, and our study with three buildings shows that weak supervision can reduce the time to annotate the metadata in building systems by a factor of 4. [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]

• A study on housing stock improvement potential in Mediterranean areas is presented. • Three typical case studies were chosen from the stock of a southern Spanish city. • Monitoring on environmental and electricity data and tests logged real performance. • Energy models were built and adjusted based on real occupancy conditions. • The considered building retrofits raise indoor temperature and reduce discomfort. Renovating buildings is one of the major targets within the current European goals toward decarbonized cities in 2050. The residential stock offers extensive opportunities to achieve current energy horizons, since more than 75% of the existing buildings in the European Union are energy deficient, with poorly insulated thermal envelopes which drive indoor conditions far from current comfort standards. This paper aims to provide a protocol for forecasting the upgrading potential of this housing stock in southern Europe, particularly in Mediterranean areas, identifying the level of obsolescence and possibilities for improvement, especially in winter indoor thermal conditions, offered by standard passive strategies. Following an architectural, constructive, and energy analysis of common patterns throughout the urban stock of the southern Spanish city of Córdoba, three case studies built-in 1951–1980 – pre-dating the first Spanish regulations on thermal conditioning in buildings – were selected for the assessment. Long-term monitoring and in situ tests (i.e., infrared thermography, airtightness) recorded the performance of these buildings under real occupancy conditions, subsequently incorporating them into the calibration of simulation energy models, aiming to predict the potential improvements after implementing standard passive strategies in these buildings. Results show that building retrofits not only improve the quality of indoor thermal conditions by increasing indoor temperatures by 3–4 °C but also almost eliminate occupied hours outside the comfort range. [ABSTRACT FROM AUTHOR]

This paper exploits a whole-building energy simulation approach to develop and evaluate demand response strategies for commercial buildings. The research is motivated by the increasing penetration of renewable energy sources such as wind and solar, which owing to their stochastic nature, means that enhanced integration of demand response measures in buildings is becoming more challenging and complex. Using EnergyPlus, a simulation model of a multi-purpose commercial building was developed and calibrated. Demand response strategies are evaluated for a number of building zones, which utilise different heating, cooling and ventilation equipment. The results show that for events of varying demand response durations, different strategies should be selected for each zone based on their thermal and usage profiles. Overall, a maximum reduction of 14.7% in electrical power demand was recorded when targeting a centralised chiller load, with smaller reductions for other decentralised building loads. [ABSTRACT FROM AUTHOR]