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Much research has been focused on maintaining stable humidity conditions in buildings housing heritage collections while reducing energy use. Moisture buffering by collections themselves can have a marked effect on the stabilisation of relative humidity (RH), the key parameter for preservation. Modelling of moisture transport using COMSOL Multiphysics was applied to transform three-dimensional paper and wooden objects into their one-dimensional representations, without changing the moisture uptake and release characteristics. The results were coupled to the modelling of indoor microclimate and energy consumption in collection storage spaces with the use of WUFI ® Plus software. The study revealed the crucial impact of air exchange rate of the building on the stability of indoor RH and the humidification and dehumidification loads required to maintain it. In the adequately air-tight library store, a sizeable paper collection was found to reduce the RH fluctuations from ±9% to ±6% around the yearly average and the energy consumption due to the humidification and dehumidification load by 38% when compared with the empty space, for a high-quality climate control scenario. In turn, a wooden collection, occupying a realistic fraction of a museum store was not large enough to significantly narrow down the RH variations and reduce the energy consumption. [ABSTRACT FROM AUTHOR]

In an era of increasing energy demands and environmental concerns, optimizing energy consumption within buildings is crucial. Despite the vast improvements in HVAC and lighting systems, plug loads remain an under-studied area for enhancing building energy efficiency. This paper studies smart plug active operating mode detection, plug-level load forecasting, and plug scheduling methodologies. This research leverages a unique dataset from the University of California, San Diego, consisting of readings from over 150 smart plugs in several office buildings for more than a year, notably during the post-Covid era. This dataset is made publicly available. A comprehensive literature review on plug, i.e. , appliances operating mode detection is presented. Novel unsupervised learning approaches are applied to identify plug operating modes. A pipeline integrating the detected modes with forecasting and scheduling is developed, aiming at building energy consumption reduction. Our findings offer valuable insights and promising results into smart plug management for energy-efficient buildings. • A novel year-long dataset of power readings from 169 office smart plugs is introduced. • The first review of plug load mode detection methods and terminology is presented. • The paper proposes an unsupervised method to identify plugs load operating modes. • The proposed smart plug scheduling pipeline reduces energy consumption by 20%. [ABSTRACT FROM AUTHOR]

Currently, building energy consumption prediction models are usually based on a large amount of historical operational data in high demands of building operating hours and monitoring systems. However, many buildings may lack operational data due to relatively limited monitoring systems, causing the failure to use data-driven methods to characterize the energy profile. In this context, transfer learning is a promising method to establish the knowledge transfer between many high-quality building operation datasets and a small amount of target building data, and to help predict energy consumption in the target building. This paper studies the possibility of employing transfer learning to achieve both short and long-term building energy consumption prediction. Firstly, a similarity analysis method, based on variable modal decomposition and dynamic time warping, is proposed for identifying the source buildings with the most similar energy features to the target building. Then, transfer learning for long-term prediction air-conditioning energy consumption is developed with weather parameters generated by the Morphing method as inputs. For the short-term single-step prediction, the proposed model CV-RMSE improves 81.3% (AEC) and 77.4% (EEC), respectively, compared to the prediction model that does not implement the transfer learning strategy and directly uses the target BEC data. As for the short-term multi-step prediction, the proposed model CV-RMSE improves 62.0% (AEC) and 65.5% (EEC), respectively. For the long-term prediction, the average CV-RMSE for the whole year is 6.62% and 11.15% for the proposed and directly target domain-based model, respectively. The proposed method explores the practicality of transfer learning in building energy forecasting, contributing to the use of existing building operation data for energy management at different timespan. • A similarity analysis method for building energy consumption data based on VMD-DTW is proposed. • The Seq2Seq-Transformer model is applied to short-term multi-step prediction of building energy consumption. • Transfer learning idea is first introduced to the long-term building energy consumption prediction. [ABSTRACT FROM AUTHOR]

The rapid development of urbanization in China inevitably leads to a sharp increase in building energy consumption, and Passive House (PH) have received much attention in China in recent years. In this paper, the annual energy demand (AED) and summer discomfort hours (SUH) of PH are used as optimization objectives to establish a set of design solutions for multi-objective optimization (MTO) and economic analysis of a PH in Shandong Province, China. It is found that (1) the algorithm based on nearest neighbor component analysis (NCA) feature extraction applies to the sensitivity analysis of multivariate nonlinear high-dimensional data, and the NCA-GA-RBF neural network technique is proposed, which has significantly improved the goodness of fit and generalization ability for AED and SUH. (2) The NSGA-II genetic algorithm is used for the optimization search, and the results show that the overall improvement rates of AED and SUH are 25.5% and 21.6%, respectively, among the nine solutions after comprehensive evaluation; (3) Finally, the optimal scheme of energy saving and the optimal scheme of the economy are selected for economic evaluation. By comparing the economics of the two options and the improvement rates of AEDs and SUHs, the guidelines for design decision-makers are provided. In summary, the design scheme of this paper is forward-looking and reference-oriented and provides some reference suggestions for the development of PH in China. • The NCA algorithm is suitable for multivariate nonlinear sensitivity analysis. • NCA-GA-RBF has stronger and more stable generalization ability and accuracy. • In this paper, climate is the main factor affecting thermal parameters. • Global costs have a stronger economic interpretation. [ABSTRACT FROM AUTHOR]

In this paper, we estimate the energy efficiency of educational buildings with the case study of buildings in City University of Hong Kong by constructing an energy demand stochastic frontier model. The model is estimated by using the university statistical data from 2011 to 2015. For the consistent frequency of data among the variables, we have adopted the quadratic-match sum method to convert annual university report data into a monthly dataset. Our result shows the average energy efficiency is 0.87, implying that 13% of total energy consumption can be saved. We then calculate how much of the energy saving potential has been achieved by constructing the performance score, which increases from 0.08 to 0.17. It implies that the campus performs more efficiently in saving energy over time. We further develop econometric decomposition analysis based on the energy demand frontier model to identify the factors affecting energy consumption. It suggests that research activities account for a large share of overall energy consumption. Analysis on energy end-use shows university should improve efficiency in lab instrument as it is least efficient among the four usages. We expect this paper can provide the fundamental and methodological guideline for university-scale energy efficiency estimation. • Estimating energy efficiency of educational buildings needs to consider the composition of research related factors. • Energy efficiency of campus building presents increasing tendency with seasonal cycle over time. • Research related variables affect significantly on energy efficiency of educational buildings. • Energy use in lab instrument needs to be effectively managed to save energy. [ABSTRACT FROM AUTHOR]

As the building stock is projected to double before the end of the half-century and the power grid is transitions to low-carbon resources, planning new construction hand in hand with the grid and its capacity is essential. This paper presents a method that combines urban building energy modeling and local planning of renewable energy sources (RES) using an optimization framework. The objective of this model is to minimize the investment and operational cost of meeting the energy needs of a group of buildings. The framework considers two urban-scale RES technologies, photovoltaic (PV) panels and small-scale wind turbines, alongside energy storage system (ESS) units that complement building demand in case of RES unavailability. The urban buildings are modeled abstractly as "shoeboxes" using the Urban Modeling Interface (umi) software. We tested the proposed framework on a real case study in a neighborhood in Chicago, Illinois, USA. The results include estimated building energy consumption, optimal capacity of the installed power supply resources, hourly operations, and corresponding energy costs for 2030. We also imposed different levels of CO2 emissions cuts. The results demonstrate that solar PV has the most prominent role in supplying local renewables to the neighborhood, with wind power making only a small contribution. Moreover, as we imposed different CO2 emissions caps, we found that ESS plays an increasingly important role at lower CO2 emissions levels. We can achieve a significant reduction in CO2 emissions with a limited increase in cost (75% emissions reduction at a 15% increase in overall energy costs). Overall, the results highlight the importance of modeling the interactions between building energy use and electricity system capacity expansion planning. • Optimization framework combines urban building energy modeling and local planning of renewables. • Urban neighborhood buildings are modeled in an abstracted manner as "shoeboxes". • Considering local wind and solar resources along with energy storage and grid supply. • PV has the most prominent role in supplying local renewables to the neighborhood. • Substantial reduction in CO2 emissions (75%) can be achieved with limited increase in energy costs (15%). [ABSTRACT FROM AUTHOR]

The energy crisis caused by climate change is the most significant challenge of the 21st century. Single-objective and multi-objective optimization studies for buildings often only consider the building itself, without considering the impacts of climate change. However, in the future, energy-efficient design and appropriate operating parameters will be crucial considerations in the face of global climate change. This paper aims to discuss the impact of climate change in the humid-hot region over the past 60 years on the energy consumption of office buildings and propose countermeasures. Firstly, the meteorological data of Guangzhou from 1960 to 2020 is statistically analyzed, and four typical meteorological years and corresponding air conditioning design days are established. Secondly, Energyplus is used to simulate the building's energy consumption, and the energy consumption factors are analyzed for sensitivity using jEplus. Finally, a multi-objective optimization is performed using jEPlus + EA with the Non-dominated Sorting Genetic Algorithm (NSGA-II). The results found that climate change led to a significant increase in the duration of high outdoor temperatures during summer. Specifically, the daily dry-bulb temperature for air conditioning design increased by 0.4–0.7 °C, and due to climate change, the energy consumption of the office building increased by about 4.6 %. The main factor affecting the cooling energy consumption in summer was the indoor cooling set point (8.29%–30%), infiltration air volume (1.0%–8.6%), fresh air volume (3.2%–16.06%), the glazing solar transmission rate (1.25%–9.0%), building orientation (0%–1.43%), and relatively small influence of envelope insulation performance. The operation scheme determined by multi-objective optimization can reduce the cooling energy consumption of the building by 10.6%–16.8%, which can mitigate the rise in energy consumption resulting from climate change to a greater extent. [Display omitted] • Discuss the impact of climate change over the past 60 years on the energy consumption of office buildings. • There was a significant increase in the duration of high outdoor temperatures during the summer. • Multi-objective optimization is performed using jEPlus + EA. • Climate change caused energy consumption to increase by about 4.6 %. • Optimal solutions can reduce the cooling energy consumption of the building by 10.6%–16.8%. [ABSTRACT FROM AUTHOR]

Abstract In 2011, the Danish government published an energy strategy for Denmark where one of the main targets are that Denmark should be a fossil-free society by 2050. Calculations show, that in order to reach this goal it is necessary to reduce the energy consumption of the existing building stock by 50% on average. Since a 50% reduction is obviously not possible for all buildings, those that can should aim for a so-called "deep energy renovation", i.e. reducing the energy consumption to a level corresponding to that of new buildings or even more. This paper describes two case studies where multi-story apartment buildings have undergone deep energy renovation. "Traneparken" where the expected energy use after renovation aimed at a level corresponding to that of a new building according to the Danish Building Regulations from 2015. "Sems Have" where the aim was to go even further and meet the requirements expected for new buildings in 2020, i.e. corresponding to the Danish "nearly zero energy"-definition according to the EPBD. The paper reports on calculations and measurements of energy savings, the economy of the projects and looks at the added benefits or co-benefits that residents, housing association and society in general have achieved in addition to significant energy savings. [ABSTRACT FROM AUTHOR]

Highlights • A total of 925 papers were searched and 106 articles were screened for deep review. • Green buildings generally performed better than the conventional counterparts. • A significant gap between the designed and operational energy use was found. • The key findings of objective IEQ and occupant satisfaction were summarized. • The performances of green buildings in China and the U.S. were compared. ABSTRACT This paper aims to review the published researches on post-occupancy performance of green buildings in terms of energy use, indoor environment quality (IEQ) and occupant satisfaction. A total of 925 related papers were searched and 106 articles were screened for deep review. The study also compared the actual operating performance of green buildings in China and the United States, with information collected from previous research. The findings of literature review can be summarized as follows: 1) Though with some exceptional cases, the energy performance of green buildings was on average better than the conventional counterparts with similar climate conditions; 2) A significant gap between the designed and operational energy consumption was found, and lots of green buildings saved less energy than expected; 3) No clear relationship between the actual energy use and the green building certification level could be observed; 4) The actual IEQ conditions of green buildings were not comparable in different countries due to the variance in design criterions and operation modes; 5) Green buildings generally had higher occupants' satisfaction level than conventional buildings, although the subjective data from the U.S. did not support this inference. Furthermore, this study proposed two areas worthy further investigations in the field of post-occupancy evaluation on green buildings, including new data collection technologies and performance optimization at the comprehensive level. [ABSTRACT FROM AUTHOR]

Highlights • This paper uses building structural attributes such as shapes of buildings, relative compactness(RC), glazing area(GA), roof area(RA), surface area(SA), wall area(WA), orientation(OR), overall height(OH) and glazing area distribution(GAD) for prediction of heating and cooling load. • The paper explores the data set and features to get insights with feature correlation, mutual information their association strengths etc and their relation with heating and cooling load. • The paper proposes novel group of methods based on emerging machine learning method extreme learning machine(ELM) which has shallows architecture. • The paper proposes novel method based on ELM variant online sequential extreme learning machine(OSELM) for heating and cooling load prediction in online and adaptive environment. This model also helps when data is trained in chunk by chunk fashion. • The paper demonstrates several models based on combination of learning mode, activation function and features sets and compare their results with existing work extensively. Abstract In the present day environment, smart buildings require optimization of energy consumption through monitoring, consumption prediction and making policy decisions accordingly. Attributes related to building design and structure play a vital role in heating load(HL) and cooling load(CL) of the building which directly affects the energy performance of the buildings. For prediction of HL and CL, emerging machine learning approaches can help in improving accuracy and efficiency in real time. This paper provides improvements in energy load assessment of the buildings. It is the first is the in-depth study and analysis of design and structural attributes and their correlation with HL and CL, the novel methods based on ELM and its variants online sequential ELM(OSELM) to predict HL and CL. This study also proposes OSELM based online/real-time prediction when data is coming in stream The total 24 models have been developed including 12 models based on ELM and 12 models based on OSELM with different feature sets and activation functions. Models have been compared on the basis of accuracy, computational performance and efficiency with few existing models. The experimental results show that the proposed models learn better and outperform other popular machine learning approaches such as the artificial neural network(ANNs), support vector machine(SVM), radial basis function network(RBFN), random forest(RF) and existing work in the energy and building domain. [ABSTRACT FROM AUTHOR]

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

Energy efficiency and carbon mitigation are important issues in modern energy systems research. Considering the couplings between energy and carbon, this paper investigates a carbon-embedded energy coordination problem in a park-level integrated energy system (PIES). Firstly, based on energy hub, a multilateral interactive transaction framework is introduced, consisting of energy hub operator (EHO), building users with photovoltaics, and an electric vehicle (EV) charging agent. Secondly, a time-varying carbon emission measurement model for the outsourced electricity is designed, taking into account the dynamic composition of the supply-side generators. Based on the time-varying carbon factor and virtual carbon emission flow, an energy-carbon pricing strategy is proposed to balance the carbon responsibility among different participants. Then, a multi-agent interactive trading model in the PIES is constructed, where the energy consumption plans of building users and EVs are guided by the dynamic energy-carbon integrated prices. Through the iterative interaction among different agents, the optimal trading results are finally obtained. Simulation results illustrate the effectiveness of the proposed energy-carbon integrated pricing method in reducing carbon emissions and promoting energy sharing. • A carbon-embedded multilateral energy interaction mechanism in a PIES is discussed. • A time-varying carbon emission measurement model is established. • An energy-carbon integrated pricing strategy based on the dynamic carbon factor and virtual carbon emission flow is designed. • The impacts of energy-carbon integrated pricing on the scheduling of the PIES and carbon emission are investigated. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to air quality modeling for megacities, focusing on the Paris region for the year 2009. The simulation is conducted by coupling the transport, energy demand, and emission model OLYMPUS with the atmospheric chemical transport model CHIMERE. OLYMPUS's emission calculations are based on the representation of an urban configuration, the simulation of a synthetic population, and the calculation of transport demand and energy consumption in buildings using an activity-based statistical approach. Emissions derived from this simulation, along with biogenic and additional industrial emissions, were used as input data in the CHIMERE model to predict pollutant concentrations. CHIMERE outputs were compared with observations from the local AIRPARIF air quality monitoring network, and with a reference simulation conducted using a benchmark bottom-up emission inventory. The results indicate that the platform provides a comprehensive representation of emissions and the resulting air quality. They also highlight the excellent representation of the spatial and temporal distribution of urban pollutant concentrations in comparison with both model and observational data. OLYMPUS tends to emit more primary pollutants than the reference emission inventory in the dense urban center. However, this often improves model scores for NO 2 , and these deviations remain within the uncertainty margins set for emission inventories. The approach to emission production, rooted in the connection between urban configuration and individual behaviors, allows for the exploration of innovative air quality scenarios centered on energy consumption practices. The validation of our results furnishes the OLYMPUS-CHIMERE coupling with a dependable framework for addressing numerous research inquiries related to the impact of urban policies on air quality. [Display omitted] • Description of an air quality modeling chain for decision-making. • Integration of socio-demographics parameters in the air pollutant emissions and concentrations modeling. • Validation of the modeling chain on Paris megacity. [ABSTRACT FROM AUTHOR]

In the 21st century, urbanization has caused rapid growth in the global construction area, resulting in high building resource consumption and long-term environmental impacts. Analyzing the resource consumption of public buildings and identifying the key factors that affect the consumption of building resources is important for developing of targeted policies. In this paper, we analyzed each branch of the People's Bank of China based on energy, water, and land resource consumption as well as the structure of energy consumption. Based on resource-consumption indicators, we divided 31 provincial-level administrative regions into four resource consumption levels; 65% were in the low-to-medium resource consumption levels. Medium-sized buildings had the highest energy consumption level and a more balanced energy consumption structure. The energy consumption per capita, water consumption, and utilization rate of small-sized buildings were low. To reduce resource consumption by public buildings, the construction of large-sized buildings should be limited. • The comprehensive evaluation method is used to calculate the resource consumption of the People's Bank of China. • Regression equations were established for energy consumption and resource consumption. • Medium-sized institutions had the highest energy consumption level and a more balanced energy consumption structure. [ABSTRACT FROM AUTHOR]

Accurate building energy consumption forecasting is crucial for developing efficient building energy management systems, improving energy efficiency, and local building energy supervision and management. However, short-term building energy consumption forecasting is challenging due to highly non-smooth and volatile trends. In this paper, we present a novel methodology that combines interpretable decomposition methods with an interpretable forecasting model. We first illustrate a daily energy consumption pattern recognition (DECPR) method, which decomposes daily energy consumption patterns into interpretable energy consumption subsequences. To achieve satisfactory forecasting performance, we design the vector representation of each subsequence as a static input to the temporal fusion transformers (TFT) model. This vector representation integrates the DECPR method into the TFT model. The TFT model produces interpretable outputs, such as the attention analysis of different step lengths and the visualization of the importance ranking of exogenous variables, including meteorological data, calendar information, and the vector representation. Empirical studies demonstrate that our proposed DECPR-TFT system outperforms comparable models with a mean absolute percentage error (MAPE) of 6.11%, which is significantly lower than other models. These interpretable outputs provide valuable insights for researchers seeking to develop energy-saving operation strategies in buildings. Overall, our methodology offers a promising solution for short-term building energy consumption forecasting that can contribute to more efficient building energy management and energy-saving operation strategies. • This study proposes DECPR, an interpretable decomposition method, explaining decomposed subsequences. • TFT, a deep learning-based approach with interpretability, predicts hourly energy consumption for the next day. • The interpretability of TFT model are used to analyze the effects of exogenous variables on energy consumption • The proposed DECPR-TFT model outperforms others, with 6.11% MAPE, significantly lower than competitors. [ABSTRACT FROM AUTHOR]

The use of radiant technology in attics aims to reduce the radiation component of heat transfer between the attic floor and roof decks, gables, and eaves. Recently, it has been shown that EnergyPlus underestimates the savings using radiant technologies in attic spaces. The aim of this study is to understand why EnergyPlus underestimates the performance of radiant technologies and provide a solution strategy that works within the current capabilities of EnergyPlus. The analysis uses three attic energy models as a baseline for comparison for EnergyPlus. Potential reasons for the discrepancies between the attic specific energy models and EnergyPlus are isolated and individually tested. A solution strategy is proposed using the Energy Management System (EMS) capabilities within EnergyPlus. This solution strategy produces similar results to the other attic specific energy models. This paper shows that the current capabilities of EnergyPlus are sufficient to simulate radiant technologies in attics. The methodology showcased in this paper serves as a guide for engineers and researchers who would like to predict the performance radiant technology in attics using the whole building energy software, EnergyPlus. [ABSTRACT FROM AUTHOR]

In a traditional building design process (TDP), design variables are fixed sequentially, as opposed to integrated design process (IDP) which tends to avoid sequential design phases to create more sustainable buildings. First, a reference building is introduced and an energy model based on TRNSYS is presented to determine the energy consumption and comfort in the building. The model is validated based on energy bills, certified simulations and literature. Then, the paper performs an extended sensitivity analysis (SA) of 30 design variables with respect to different performance criteria related to energy consumption and comfort, based on a TRNSYS model. Three SA techniques were used, namely standard regression coefficients (SRC), partial rank correlation coefficients (PRCC) and Sobol indices. Results show that all three techniques yielded a similar ranking of the importance of the variables for most model outputs. Interactions between variables were identified with second-order Sobol indices. In the second part of this paper, a traditional design framework was adopted in which sets of variables were fixed sequentially. A SA was performed at each phase of the process, assuming fixed values for parameters chosen in previous design phases. Results show that fixing variables during the phases of a traditional design process tends to reduce the probabilities of finding low-energy consumption designs. Moreover, the influence of some variables was found to change during the design phases. [ABSTRACT FROM AUTHOR]

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

There has been general recognition within the construction industry that there is a discrepancy between the amount of energy that buildings actually use and what designers considered that they should use. This phenomenon is termed “The Performance Gap” and is normally associated with new buildings. However, existing and older buildings contribute a greater amount of operational carbon. In response to the Performance Gap, CIBSE have developed the TM54 process which is aimed at improving energy estimates at design stage. This paper considers how the TM54 process can also be used to develop energy management procedures for existing buildings. The paper describes an exercise carried out for a university workshop building in which design energy use has been compared with the actual building energy use and standard benchmarks. Moreover, a sensitivity assessment has been carried out using different scenarios based on operation hours of building/equipment, boiler efficiency and impact of climate change. The analysis of these results showed high uncertainty in estimates of energy consumption. If carbon challenges are to be met then improved energy management techniques will require a more systematic approach so that facilities managers can identify energy streams and pinpoint problems, particularly where they have assumed responsibility for existing buildings which often have a legacy of poorly metered fuel consumption. [ABSTRACT FROM AUTHOR]

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

[Display omitted] • Present a comprehensive review of building electricity profile models. • Distinguish the differences of building electricity profile and prediction models. • Categorize the statistical and bottom-up models for electricity use profiles. • Investigate the validation metrics in accordance with the modeling methods. • Discuss the key future perspectives of building electricity profile analysis. The building sector contributes significantly to overall energy consumption and carbon emissions. Improving renewable energy utilization in buildings is of considerable importance for their carbon neutrality. Electricity is expected to become the major form of final energy consumption in buildings; as such, understanding the features of building electricity use profiles is pivotal for building energy system design and city-scale electricity distribution network planning. This study reviews the current status and future challenges of building electricity use profile models. The paper distinguishes between electricity use profile models and electricity use prediction models. Then it comprehensively reviews current research papers, focusing on four aspects, including the temporal and spatial distribution of datasets, the distinction between statistical and bottom-up modeling methods, mathematical and engineering domain validation metrics, and applications in building energy system design and energy policy making. Key perspectives for future research are discussed in the paper, including the temporal scales of models, the aggregation of electricity use profiles, occupant-driven environment-interactive model structures, as well as the fit-for-purpose model evaluation. This is intended to inform and inspire future research on building electricity use profiles, improve the in-depth understanding and quantitative descriptions of building electricity use, and support the design of future building systems and energy distribution networks. [ABSTRACT FROM AUTHOR]

Facing the challage of energy conservation and emission reduction in the building sector, most energy saving strategies of central heating only focus on the performance improvement of building envelope, ignoring the impact of energy use behavior. This paper proposed a building energy conservation strategy based on energy use behavior analysis, which provides a perspective for in-depth exploration of building energy conservation. After investigating a typical university in the northern China, the energy use behaviors were characterized and two different DeST energy consumption models before or after considering energy use behavior were established. By comparing the results calculated by these two models, the impact of energy use behavior analysis on building energy consumption was quantitatively analyzed. The results show that the heating energy consumption and carbon emission can be reduced by 23.28% and 23% respectively through the proposed strategy based on energy use behavior. Therefore, the analysis of energy use behavior can provide a theoretical basis for heating operators to make decisions on energy supply regulation. With central cooling and heating further popularizing in the future, the research methodology of this paper has more extensive reference value for energy conservation and emission reduction strategies in the building sector. [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]

• A hybrid appliance classification model is proposed for the feature extraction in the electrical power savings of buildings. • The strategy of time series is presented in the proposed appliance classification. • A secondary ensemble structure is proposed to handle the class imbalance multiclass classification problems. • The random forest algorithm is selected as the binary classifiers in the secondary ensemble structure. • The effectiveness of the proposed model is verified on the extended PLAID dataset. The non-intrusive load monitoring, which gets the energy usage information of each appliance by analyzing the aggregative signal in the centralized point is beneficial to electrical power savings of buildings. As an important step in the non-intrusive load monitoring, the appliance classification has gained great attentions. The feature extraction, the design of classification structure and the selection of classifiers are three important parts in the appliance classification. In this paper, a hybrid appliance classification model is proposed. In the hybrid model, the time series features are utilized to depict the characters of appliances, which is different from the conventional perspective of feature extraction. A secondary ensemble structure is designed to handle the class imbalance multiclass classification problems. The class imbalance is often the case in appliance classification but no effective solutions has been put forward yet. Besides, the random forest is selected as the binary classifiers in the secondary ensemble structure. The effectiveness of the proposed model is verified on the extended PLAID dataset. The experimental results indicate that the time series features provide better overall performance compared to the widely used features, the random forest performance better than the widely used classifiers and the overall performance of the proposed model is better than that of the state of the art model. [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]

• Obtaining the optimal interpolation method for adjacent sites. • Proposing the method for revising meteorological data at city scale. • Verifying the accuracy of the method by energy consumption simulation. In the development process of building energy conservation design, the correct provision of local Typical Meteorological Year (TMY) data is a prerequisite and basic work to evaluate building energy consumption, which directly affects the fine use of outdoor data for building energy conservation. Based on the recorded and observed data of meteorological stations in China, the acquisition and correction methods of TMY were studied by mathematical statistics and target searching, and the building energy consumption simulation of urban office buildings was conducted to verify the accuracy of the method, including:(1) Reviewed the generation method for TMY and the acquisition of data.(2) Based on the existing TMY data, the best spatial interpolation method of adjacent stations is studied by using the standard deviation method, and the selection method of the best number of stations on a large regional scale is given.(3) An improved method of urban environment TMY is proposed. The dry bulb temperature and relative humidity of the building site are corrected to generate TMY under Urban Heat Island (UHI) effect. The research in this paper improves the coverage and accuracy of urban TMY data and supplies basic research support for building energy-saving designs. [ABSTRACT FROM AUTHOR]

• We decompose the building energy consumption into a global part and a local part through domain knowledge analysis. • A hybrid prediction model, combining static model and time series model, is proposed. • Proposed hybrid prediction model outperformed other machine learning models for a large margin. • The types of buildings have an influence on the model performance. The task of building energy prediction (BEP) is essential to several emerging research domains, including energy management, control optimization and fault detection. An accuracy prediction model contributes significantly to the improvement of building energy efficiency and flexibility. However, most existing prediction models are either based on single time series model or static model, making them perform poorly for long-term predictions. To solve this problem, this paper proposes a hybrid prediction model, which combines the deep ensemble (DE) model and autoregressive (AR) model together. Its motivation comes from the domain knowledge analysis of building energy consumption, where the energy consumption is decomposed into a global part and a local part. The deep ensemble model is adopted to predict its global part, and the AR model is employed to predict its local part. Comprehensive data experiments are conducted based on 50 real buildings across five building types to validate the model performance. The prediction horizon is the 24-h ahead sliding window prediction for one-year energy consumption. The results indicate that the hybrid prediction model outperforms the LSTM, DE-only, and ARIMA-only model, where its relative improvements of CV-RMSE are 28.7%, 35.98% and 18.47%, respectively. The experimental results also reveal that the types of buildings, i.e., office, lodging, will affect the model performance, which is attributed to their different user-behaviors. Based on the experimental results, it is demonstrated that the integration of static model and time series model is robust to the varying of building types and prediction steps, which should be the preferred choice of BEP task. [ABSTRACT FROM AUTHOR]

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

Building energy consumption forecasting is crucial for energy managers and policymakers. However, acutely predicting the energy consumption of any building is more important in energy efficiency benchmarking of buildings. Energy benchmarking is a precise technique for measuring, tracking, and reducing end-use energy usage of buildings by employing comparative scenarios. Bottom-Up energy consumption assessment has been used in several studies to evaluate a collection of buildings' energy performance to a standard. Traditionally, researchers have used a single approach to predict the energy use of buildings and to conduct benchmarking. However, it has been found that doing so frequently compromises the accuracy of the predicted energy use of buildings. It also has an adverse effect on the buildings' energy benchmarking framework results. This paper will provide an energy benchmarking framework for residential buildings based on the blending techniques of multiple approaches. Over 3000 datasets were gathered from the Indian city of Jaipur. In order to analyse the trends in energy consumption, the data were divided into categories based on economic levels. The data is then subjected to some fundamental statistical analysis, along with several approaches are blended to forecast the energy consumption of the buildings with the highest accuracy. These benchmark levels set guidelines for assessing and recognising strong performance while also identifying underperforming buildings and giving them more priority for energy efficiency improvement. The technique may be used for different metropolitan levels and climatic conditions. [ABSTRACT FROM AUTHOR]

There has been an increasing concern on thermal comfort in sleeping environments and its associated energy use in the past few years. To improve the thermal environment and to reduce energy use of air conditioning in bedrooms, applications of task/ambient air conditioning (TAC) systems were proposed and studied previously. It’s indicated that the TAC system can be well integrated with a bed. Due to the variation of the envelope heat gain in a bedroom during night, it is necessary to study the thermal environment inside a bedroom and the energy use of a TAC system for the bedroom at varying envelope thermal loads. Therefore, this paper reports on a numerical study on a TAC system applied to a bedroom with different envelope heat gains. The influences of envelope heat gain on energy consumption of the TAC system and the indoor thermal comfort were studied in this paper. The research results show that at supply air flow rate ( Q s ) of 50 l/s, energy consumption was increased from 47.78 W to 213.11 W, and the PMV value was increased from −1.69 to −1.29 with the increase in envelope heat gain from 3.11 W to 155.6 W. To make PMV equal to zero under different envelope heat gains, linear regression models between supply air parameters and PMV were built and solved. The obtained values form curves where PMV = 0 and give a guide for operation of the TAC system. It was found that the calculation of energy consumption on these curves depends only on envelope heat gain. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel approach to control CO 2 and H 2 O composition in an enclosed environment. • A cyclic CO 2 adsorption process and dehumidication units are considered. • The mathematical model shows superior performance of the proposed system. • Carbon footprint of the system is discussed with scale-up study. • Influence of parametric uncertainties in the model is discussed. Rapid growth in global energy consumption has raised concern on the environmental impacts such as ozone layer depletion and climate change. Enclosed space, such as commercial buildings, accounts for about 40% of global energy consumption and the demand is constantly increasing due to increasing population, urbanization, and economic development. The energy demands in the building sector calls for strategic measures to develop energy efficient technologies. This paper presents a strategy to decrease energy demands inside buildings by proposing a ventilation system which regulates the enclosed air quality resulting in reduced air conditioning. The system consists of multiple adsorption beds with zeolite 13X monoliths for CO 2 removal, and silica gel for humidity control, inside the enclosed space. The air conditioning system results in decrease in energy requirement and improvement in economics by 55% as compared with conventional ventilation system. The model is scaled up to the size comparable with total office inventory of New York City, and the reduction in carbon emissions by introducing the air composition control system for New York City is equivalent to replacing 57 million incandescent light bulbs by LEDs. This paper concludes that the air conditioning system proposed in this study results in the improvement in performance as compared to a conventional ventilation system and could reduce energy consumption inside commercial buildings. [ABSTRACT FROM AUTHOR]

• A novel vector field-based SVR for building energy consumption prediction (ECP) is proposed. • Nonlinearity of the ECP models is transformed into linearity via vector field-based SVR. • Two high precision, generalization capability, and robustness energy consumption prediction models are established. • A real case study demonstrates the superiority of this study compared with previous studies. Building energy consumption prediction plays an irreplaceable role in energy planning, management, and conservation. Data-driven approaches, such as artificial neural networks, support vector regression, gradient boosting regression and extreme learning machine are the most advanced methods for building energy prediction. However, owing to the high nonlinearity between inputs and outputs of building energy consumption prediction models, the aforementioned approaches require improvement with regard to the prediction accuracy, robustness, and generalization ability. To counter these shortcomings, a novel vector field-based support vector regression method is proposed in this paper. Through multi-distortions in the sample data space or high-dimensional feature space mapped by a vector field, the optimal feature space is found, in which the high nonlinearity between inputs and outputs is approximated by linearity. Hence, the proposed method ensures a high accuracy, a generalization ability, and robustness for building energy consumption prediction. A large office building in a coastal town of China is used for a case study, and its summer hourly cooling load data are used as energy consumption data. The results indicate that the proposed method achieves better performance than commonly used methods with regard to the accuracy, robustness, and generalization ability. [ABSTRACT FROM AUTHOR]

Highlights • The paper studies the impact of urban parks on reducing urban temperature. • Findings reveal that urban park construction reduces building energy consumption. • There is a limit distance to the reduction of energy consumption due to urban parks. • Change of urban characteristics due to park construction affects energy consumption. Abstract A change in the thermal environment of an urban area affects health, living conditions, and energy consumption. In urban planning, urban parks are one of the methods for improving the thermal environment and saving cooling energy. Urban park construction can mitigate temperature, but it also causes urban development by increasing local attractiveness. To achieve efficient energy saving through parks in urban planning, the purpose of this study is to investigate the relationship between building energy consumption and urban characteristics both before and after the construction of an urban park. This study targeted Seoul's Gyeongui line forest, which was recently converted into a linear park on the former railway as an urban regeneration project. We analyzed the relationship between energy consumption and urban characteristics using a regression model, focusing on the changes before and after the construction. In this study, urban characteristics include environment, building physical characteristics, and economic variables. The results show that the construction of the urban park reduced not only temperature but also building energy consumption. The energy reduction effect of the park was limited to a marginal distance. Meanwhile, the urban park construction caused land prices to rise and prompted new development, and this changed the urban characteristics of the area and affected energy consumption. Despite changes in urban characteristics, urban park planning is an effective methods of reducing the energy consumption involved in cooling urban areas. We recommend comprehensive consideration of the urban factors when making park policy to reduce urban temperature and energy consumption. [ABSTRACT FROM AUTHOR]

Abstract Considerable efforts have been made to reduce buildings' operational energy use over the last decades, but little attention has been paid to reduce the material transportation and construction energy. Focusing only on the operation phase forgoes the opportunity to reduce other building-related energy consumption, and even if the environmental impacts arising from construction and transportation are small as compared to the operation phases, its cumulative impact at the national level is of concern. The energy consumed by a building is divided into two parts embodied energy and operation energy. Further, the embodied energy is constituted of energy intensity of materials, energy consumed during transportation and energy consumed for construction. This paper proposes a methodology to integrate embodied energy consumption into a BIM platform and provides a seamless analysis based on available information. Plug-ins are developed to fulfill a convenient linkage between the BIM model and external databases. Simulation models are created, which can be used as templates for energy optimization during transportation and construction. By analyzing different resource combination scenarios, lower energy consumption can be achieved. [ABSTRACT FROM AUTHOR]

Most building energy simulation (BES) tools need detailed inputs for the modeling process because of the nature that building is a complex system and evaluating its performance usually involves many factors and uncertainties. In this research, a lightweight building energy simulation tool – SimBldPy, is proposed. The proposed simplified hourly method in this paper includes additional thermal resistances of the zone internal floor and internal walls coupled with adjacent zone temperature. Two DOE reference building types (one residential and one office) located in Philadelphia and San Francisco are used in the research to verify the performance of the tool. Its performance and validity in simulating hourly energy use with different heating and cooling set points in each zone, under various climate conditions, and with multiple ECMs being applied to the building, have been tested and verified with EnergyPlus. This tool and modeling method could be used for rapid modeling and assessment of building energy for a variety of ECM options. By using this tool in modeling an on-campus building in University of Pennsylvania, it is shown that the calibrated building model in SimBldPy can be used to model the energy use of an existing building. With low cost in computation, the tool is able to provide rapid modeling and assessment of building energy use (BEU) for parametric studies. [ABSTRACT FROM AUTHOR]

In Germany, policy-induced energy efficiency improvements typically aim at reducing primary energy consumption. Private households, on the contrary, pursue the maximization of wellbeing or, in microeconomics jargon, the maximization of utility of the occupants. There is a marked difference between upfront-calculated energy performance ratings (EPRs) and actual heat energy consumption (HEC). From an energy and environmental policy point of view, deviations of energy consumption from ex ante calculated EPRs is problematic, as it jeopardizes their usefulness for (prospective) homeowners, policy-makers and researchers relying on EPRs as a guidance. The EPR-HEC gaps reported are, apart from deviations from the mean aggregate values (i.e. heterogeneity), often attributed to anticipated or unanticipated behavioral effects. From an energy economist's point of view, energy rebound induced by lowered costs per unit of energy service due to increased energy efficiency is one explanation. The related literature almost entirely treats building-specific EPRs as universal standards, trying to explain the empirically observed discrepancies. In this paper, we investigate to what extent the current EPR scheme in place in Germany today can address behavioral issues. To this end, based on two different multi-family housing data sets for Germany and regression analysis, we empirically investigate the deviations between EPRs used in the regulatory norms and the HEC levels observed in reality. We find evidence that it is not necessarily the behavioral dimension, but rather the static and mostly technically guided calculation of the EPRs itself that accounts for the major part of the deviations. We conclude that there is indeed a need for further improvements in the field of EPR regulation and methodology. [ABSTRACT FROM AUTHOR]

In the US, building sector consumes approximately 41% of all U.S. primary energy and 70% of all generated electricity. Office building uses the largest percentage of primary and derived energy in the commercial buildings sector. Therefore, energy saving is the primary target of building renovation and equipment retrofit. To estimate energy savings after a renovation or equipment retrofit, measurement and verification (M&V) must be conducted based on energy use models or benchmark tools to track and assess savings. Although some methods and modeling tools are applied more often, there is no well-accepted model formulation methodology and some methods have high uncertainty. This paper discusses a method to formulate the energy use model as an inverse regression model via an advanced and robust high-dimensional data analysis method. According to the inverse regression model, independent variables determined to be most influential to the energy consumption should be metered prior to building retrofit or audit. To select the most influential variables, this research considers a comprehensive set of potential key variables to avoid underspecified model issue. The models established by this method are observed to have great power in predicting the building energy consumption. More importantly, through the variable selection procedure, the method identifies key variables that should be monitored to continuously improve the building energy performance. A simulation study and a case study are presented to show the effectiveness of the new approach. [ABSTRACT FROM AUTHOR]

Clustering methods are often used to model energy consumption for two reasons. First, clustering is often used to process data and to improve the predictive accuracy of subsequent energy models. Second, stable clusters that are reproducible with respect to non-essential changes can be used to group, target, and interpret observed subjects. However, it is well known that clustering methods are highly sensitive to the choice of algorithms and variables. This can lead to misleading assessments of predictive accuracy and mis-interpretation of clusters in policymaking. This paper therefore introduces two methods to the modeling of energy consumption in buildings: clusterwise regression, also known as latent class regression, which integrates clustering and regression simultaneously; and cluster validation methods to measure stability. Using a large dataset of multifamily buildings in New York City, clusterwise regression is compared to common two-stage algorithms that use K -means and model-based clustering with linear regression. Predictive accuracy is evaluated using 20-fold cross validation, and the stability of the perturbed clusters is measured using the Jaccard coefficient. These results show that there seems to be an inherent tradeoff between prediction accuracy and cluster stability. This paper concludes by discussing which clustering methods may be appropriate for different analytical purposes. [ABSTRACT FROM AUTHOR]

The building sector is considered as the biggest single contributor to world energy consumption and greenhouse gas emissions. Therefore, a good understanding of the nature and structure of energy use in buildings is crucial for establishing the adequate future energy and climate change policies. Availability of the updated data is becoming increasingly important in order to allow a rigorous analysis. In this paper, recent data on the world energy consumption in both residential and commercial buildings are reported. Past situation, current status and future trends are discussed and analyzed for selected countries. A breakdown of buildings energy consumption is realized in order to determine the influencing key parameters. A whole section of this paper is dedicated to give an overview of measures and policies adopted by different countries, allowing the monitoring, management and reduction of the energy consumption in buildings. Critical aspects of these policies are discussed based on the feedback of the early adopters. [ABSTRACT FROM AUTHOR]

Linked Data is the key paradigm of the Semantic Web, a new generation of the World Wide Web that promises to bring meaning (semantics) to data. A large number of both public and private organizations have published their data following the Linked Data principles, or have done so with data from other organizations. To this extent, since the generation and publication of Linked Data are intensive engineering processes that require high attention in order to achieve high quality, and since experience has shown that existing general guidelines are not always sufficient to be applied to every domain, this paper presents a set of guidelines for generating and publishing Linked Data in the context of energy consumption in buildings (one aspect of Building Information Models). These guidelines offer a comprehensive description of the tasks to perform, including a list of steps, tools that help in achieving the task, various alternatives for performing the task, and best practices and recommendations. Furthermore, this paper presents a complete example on the generation and publication of Linked Data about energy consumption in buildings, following the presented guidelines, in which the energy consumption data of council sites (e.g., buildings and lights) belonging to the Leeds City Council jurisdiction have been generated and published as Linked Data. [ABSTRACT FROM AUTHOR]

• The solar louver was proposed to increase direct heat gaining for building. • Effects of blades' angle and characteristics on heat gain of solar louver was quantified. • Indoor temperature was higher and temperature field was more uniform with coupled solar louver. • By comparison with non-coupled solar louver, solar louver in maximum light transmittance mode decreased active heating supply capacity by 33.8%. Promoting solar energy for heating is of great significance for achieving the national goals of "double carbon" goals. Increasing the heat gain of window space could improve the solar energy utilization in congested urban environments. The paper proposed a new heating type for active solar house by solar louver. Thermal effects validation on solar louver were studied by field-test and simulation analysis. A solar louver model was established on the platform of MATLAB and TRNSYS. The simulated results showing that heat gain of solar louver reached the largest when blades' angle was about 10°. Solar louver with high reflectivity and low thermal emissivity could capture more heat gain. The in-situ experiments of active solar house coupling solar louver were carried out in Ordos. From 24th to 29th Jan. 2022, all-day comparative experiments of thermal effects in actual winter-weather were conducted. The experimental results indicating that solar louver in maximum light transmission mode was preferred in active solar house. Compared with the condition of solar louver in overlapped light transmittance mode, maximum light transmission mode could obtain 17.7% higher heat gain. Indoor temperature was more higher and temperature field was more uniform. Moreover, by comparison with the non-coupled solar louver, solar louver in maximum light transmittance mode decreased active heating supply capacity by 33.8%, which could reduce energy consumption of building. The main work here provided a theoretical basis for performance optimization of active solar house. [ABSTRACT FROM AUTHOR]

• A regional integrated energy system with multi-region energy sharing and storage is constructed. • A co-optimization method compromise system configuration and operation dispatch is proposed. • The overall benefit of the RIES in a nearly-zero energy region scenario is studied. • The role played by energy sharing and storage in renewable energy utilization is discussed. The nearly-zero energy community composed of contiguously nearly-zero energy buildings can effectively reduce the operational energy consumption of buildings by adopting appropriate active and passive technologies, which is the main development direction of future buildings. However, there are few studies on the collaborative optimization of nearly-zero energy community supply systems. Therefore, a nonlinear cooperative optimization model of the regional integrated energy system with multi-region energy sharing and multi-energy storage is constructed in this paper. A two-layer collaborative optimization method is proposed, which optimizes the upper layer's renewable energy and energy storage capacity and the operation dispatching in the underlayer. Finally, the overall benefit, typical daily energy scheduling, and the energy sharing and storage impact on renewable energy utilization of the system when it supplies energy to a nearly-zero energy community are studied. The research results show that compared with the isolated integrated energy system, the supply cost, primary energy consumption, carbon emission and interactive power per unit area of the regional integrated energy system are reduced by 3.45 CNY/m2, 3.95 kWh/m2, 1.35 kg/m2 and 1.66 kWh/m2, respectively. In addition, multi-region energy sharing and multi-energy storage can effectively improve the self-consumption/sufficiency ratio of photovoltaic and wind power generation and solar thermal collector heat collection. This study provides a feasible solution for applying nearly-zero energy communities in regional integrated energy systems. [ABSTRACT FROM AUTHOR]

• BIM-DB can effectively simulate building performance parameters. • The PSO-RF-NSGA-III hybrid method can be used to predict and optimize building performance. • NSGA can simultaneously optimize energy consumption, carbon emissions and thermal comfort. • The optimal building parameters are different in different regions. Near-zero-energy-consumption buildings (NZEBs) are of great significance for sustainable development, and their design and research have attracted increasing academic attention. To drive and realize the energy saving design and achieve carbon emission and thermal comfort optimization of NZEBs, in this paper, an intelligent optimization method integrating the BIM-DB and PSO-RF-NSGA-III method is established. Multiobjective optimization problems involving NZEBs in four typical climate regions in China are explored. With a typical office building as an example, first, simulation calculations regarding the energy consumption, carbon emissions and indoor thermal comfort in the four climatic regions are performed based on orthogonal tests and BIM-DB. Second, the nonlinear mapping relationships between building design parameters and prediction targets are constructed with the PSO-RF model, which is trained with sample data. The obtained nonlinear mapping relations are used to establish the objective function of NSGA-III, and the multiobjective Pareto-optimal solution set is obtained with the developed PSO-RF-NSGA-III algorithm. Finally, the only optimal solution is determined by using the ideal point method, and reference near-zero-energy-consumption office building parameters are calculated for different climate regions. The conclusions are as follows. (1) The PSO-RF algorithm can efficiently predict building energy consumption, carbon emissions and thermal comfort. In the four regions, the goodness of fit of the three targets is greater than 0.94. (2) Multiobjective optimization can be performed with the proposed RF-NSGA-III intelligent optimization method. After optimizing multiple groups of optimization schemes and adopting energy saving measures, the energy consumption levels in the four climate regions are reduced by 39.72 %, 32.22 %, 26.94 % and 35.37 %, and the other goals are optimized. (3) Index calculations indicate that the optimized building design parameters meet the specified standards for NZEBs, and the main influencing factors and corresponding measures vary from region to region. [ABSTRACT FROM AUTHOR]

The optimization of energy consumption in buildings' HVAC systems plays a crucial role in reducing greenhouse gas emissions worldwide. In new and deeply-renovated buildings, characterized by modulating terminal units and aiming at the maximum exploitation of renewables, an accurate hydraulic balance of the distribution network may become critical, resulting in a significant increase of pumping energy consumptions and a deterioration of indoor thermal comfort conditions. In the present paper, we compare the performance of traditional manual balancing valves and new pressure independent control valves (PICVs), used to balance the hydronic loop of HVAC systems. To perform this analysis, a new MATLAB-Simulink model has been specifically developed to simulate the behavior of PICVs and has been used in an application case study. The efficiency of manual and pressure independent control valves is evaluated numerically by simulating a multi-zone distribution network under variable operating conditions and considering different control strategies of the circulating pump. Results show that in off-design conditions, when some of the branches of the hydraulic network are closed, traditional manual balancing valves are not able to guarantee the nominal mass flow rate in the remaining loops. On the contrary, no significant variations of water mass flow rate are observed when PICVs are adopted, even in partial load conditions. Despite their additional cost, PICVs allow to ensure better indoor thermal comfort sensations for individuals, avoiding under/over-heating of rooms and, moreover, yield a decrease of the pump electric consumption with respect to traditional manual balancing valves. In addition, based on the obtained results, general rules concerning the adoption of PICVs are provided, depending on the extension of the hydraulic loop and the adopted pump control logic. The reported results highlight the role that PICVs can play to ensure energy savings in HVAC systems without penalizing users' indoor comfort conditions. [ABSTRACT FROM AUTHOR]

• Provided a platform to test various residential building control systems. • Used probability of occupancy to determine setback temperature when unoccupied. • Integrated optimizer with adaptive and occupancy control. • Generated dynamic electricity tariff based on wholesale electricity pricing. • Evaluated the peak load shaving potential with dynamic pricing and optimization. Energy consumption in buildings continues to rise with increased deployment of energy-consuming equipment such as Heating, Ventilation, and Air Conditioning (HVAC) amid a growing world economy. Renewable energy is projected to comprise a majority of the future electricity supply, but the intermittent nature of renewables means that consumption must respond to dynamic supply for optimal utilization. This paper proposes a novel HVAC control strategy for residential buildings using the adaptive comfort model, considering occupancy through probability and real-time information, and optimizing the HVAC schedule to reduce cost, maintain thermal comfort, and respond to the dynamic availability of renewable energy while being generalizable to different situations. To validate this approach, the Universal CPS Environment for Federation (UCEF) co-simulation platform is used to connect advanced building controls with the building energy simulation software EnergyPlus. Simulations are performed for a residential building in Sacramento, CA during a typical summer week. Economic impacts, energy consumption, and thermal comfort are analyzed for traditional, adaptive, and occupancy-based control strategies under demand-based, tiered, and fixed electric tariff systems. Simulation results show that occupancy consideration, adaptive thermal comfort, and optimization can reduce cost by 50.1 %, electricity consumption by 52.9 %, and discomfort by 56.2 % compared to traditional fixed setpoints. The ability of the proposed HVAC control strategy to shift energy consumption away from peak times under a demand-based tariff system is qualitatively analyzed and findings suggest that maximum load-shifting on a grid-scale is attained using occupancy consideration with optimized control and demand-based pricing. For individual residential buildings, similar economic benefits can be gained using the less-complex adaptive HVAC control strategy with existing tiered or simple electric tariff systems. [ABSTRACT FROM AUTHOR]

Energy simulation, the virtual representation and reproduction of energy processes for an entire building or a specific space, could assist building professionals with identifying relatively optimal energy conservation measures (ECMs). A review of current work revealed that methods for achieving simultaneous high accuracies in different levels of simulations, such as building level and zone level, have not been systematically explored, especially when there are several zones and multiple HVAC units in a building. Therefore, the objective of this paper is to introduce and validate a novel framework that can calibrate a model with high accuracies at multiple levels. In order to evaluate the performance of the calibration framework, we simulated HVAC-related energy consumption at the building level, at the ECM level and at the zone level. The simulation results were compared with the measured HVAC-related energy consumption. Our findings showed that MBE and CV (RMSE) were below 8.5% and 13.5%, respectively, for all three levels of energy simulation, demonstrating that the proposed framework could accurately simulate the building energy process at multiple levels. In addition, in order to estimate the potential energy efficiency improvements when different ECMs are implemented, the model has to be robust to the changes resulting from the building being operated under different control strategies. Mixed energy ground truths from two ECMs were used to calibrate the energy model. The results demonstrated that the model performed consistently well for both ECMs. Specific contributions of the study presented in this paper are the introduction of a novel calibration framework for multi-level simulation calibration, and improvements to the robustness of the calibrated model for different ECMs. [ABSTRACT FROM AUTHOR]

Growing demand for urban built spaces has resulted in unprecedented exponential rise in production and consumption of building materials in construction. Production of materials requires significant energy and contributes to pollution and green house gas (GHG) emissions. Efforts aimed at reducing energy consumption and pollution involved with the production of materials fundamentally requires their quantification. Embodied energy (EE) of building materials comprises the total energy expenditure involved in the material production including all upstream processes such as raw material extraction and transportation. The current paper deals with EE of a few common building materials consumed in bulk in Indian construction industry. These values have been assessed based on actual industrial survey data. Current studies on EE of building materials lack agreement primarily with regard to method of assessment and energy supply assumptions (whether expressed in terms of end use energy or primary energy). The current paper examines the suitability of two basic methods; process analysis and input–output method and identifies process analysis as appropriate for EE assessment in the Indian context. A comparison of EE values of building materials in terms of the two energy supply assumptions has also been carried out to investigate the associated discrepancy. The results revealed significant difference in EE of materials whose production involves significant electrical energy expenditure relative to thermal energy use. [ABSTRACT FROM AUTHOR]

Accurate building energy prediction plays an important role in improving the energy efficiency of buildings. This paper proposes a homogeneous ensemble approach, i.e., use of Random Forest (RF), for hourly building energy prediction. The approach was adopted to predict the hourly electricity usage of two educational buildings in North Central Florida. The RF models trained with different parameter settings were compared to investigate the impact of parameter setting on the prediction performance of the model. The results indicated that RF was not very sensitive to the number of variables ( mtry ) and using empirical mtry is preferable because it saves time and is more accurate. RF was compared with regression tree (RT) and Support Vector Regression (SVR) to validate the superiority of RF in building energy prediction. The prediction performances of RF measured by performance index (PI) were 14–25% and 5–5.5% better than RT and SVR, respectively, indicating that RF was the best prediction model in the comparison. Moreover, an analysis based on the variable importance of RF was performed to identify the most influential features during different semesters. The results showed that the most influential features vary depending on the semester, indicating the existence of different operational conditions for the tested buildings. A further comparison between RF trained with yearly and monthly data indicated that the energy usage prediction for educational buildings could be improved by taking into consideration their energy behavior changes during different semesters. [ABSTRACT FROM AUTHOR]

In this paper, a model of homeowners’ energy efficiency retrofit choices is presented based on the database of a project implemented by the national energy efficiency retrofit scheme in Croatia during 2015 and 2016. The analysis was performed on the cleaned dataset of 4610 implemented projects in privately owned family houses. We have analyzed available data to answer how much money consumers are willing to pay for energy refurbishment and how this corresponds to household characteristics. We have developed linear regression models for estimating costs and energy savings of different measures and have further applied multinomial and nested discrete choice models on investment choices made by homeowners. The resulting models provide estimations of willingness to pay for energy efficiency refurbishment in private family houses in relation to income class the owners belong to. The results show that homeowners in all income classes have a similar level of willingness-to-pay for similar energy efficiency measures, with main differences being their capability to invest. With the current level of subsidies, only three of four observed measures show a significant level of free-riders. This suggests there could be a better allocation of public funds, where less money could be spent for subsidies to generate a similar amount of savings. Furthermore, the proposed model replicates existing experience and provides insight into inconsistencies which appear when the effectiveness of implemented measures is analyzed. [ABSTRACT FROM AUTHOR]

Occupants are integral elements of a building ecosystem and their behavior can have a substantial impact on energy consumption in buildings. A wide range of energy feedback programs have been developed to make energy consumption more visible and interpretable to occupants and help them learn how to control and save energy. In this paper, we conduct a critical review of the literature related to energy feedback and identify four key methodological approaches to designing and studying energy feedback programs: experiments, analytics, surveys and simulation. Our meta-analysis reveals five research gaps and opportunities for future methodological fusion at the intersection between such approaches, including the analytics-survey, experiments-analytics, experiments-analytics-surveys, simulation-experiments and analytics-simulation interfaces. Future research at these crucial interfaces could provide the deeper understanding necessary to develop energy feedback programs that yield substantial and persistent energy savings. [ABSTRACT FROM AUTHOR]