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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]

In a context of massive renovation of residential buildings, stakeholders need decision-support models based on knowledge of the current building stock and accurate simulation of energy demand. This paper presents a new strategy for reducing energy consumption in the building sector, a key factor in combating climate change and promoting sustainability. We introduce an approach to (1) plan retrofits at community level, with a building resolution, for different years of an optimization period and (2) assist local authorities in selecting effective measures to improve the environmental performance of their building stock. The focus is on creating trajectory retrofit plans creation for a building stock with three main retrofit options: improving insulation, heating systems and hot water systems. We adapt a complex but linear approach, a type of problem-solving structure known as a multidimensional multiple-choice knapsack problem, which manages to handle a large number of possible retrofit combinations without becoming unwieldy. The planning process is streamlined as a single-objective optimization task that aims to reduce the total cost of retrofits by reducing their net present value. The efficiency of the model is demonstrated by simulating retrofit scenarios for 4,000 buildings in a French region to prove its ability to tackle large problems. France's targets for decarbonizing the residential sector are taken into account, with a target of reducing GHG emissions by a factor of 10 and a building stock consuming 80kWhEP/m2/year. The results show that these plans are feasible, but that they will require 50% of all buildings to undergo major renovation with abatement costs of around €200/tGES. Our practical application to an actual community demonstrates the model's ability to identify appropriate retrofitting measures and compile building data. • Development of an energy optimization framework integrating a linear simulation model. • Multi-stage optimization generalizing the knapsack problem for building renovation dynamics. • Bridging the gap in energy retrofit strategies by comparing decision-maker and model proposals. [ABSTRACT FROM AUTHOR]

This paper explore the benefits of a bottom-up spatially enabled engineering building-based energy framework in identifying neighbourhoods, and community’s building aggregated areas with spatial patterns. We argue that an area-based approach allows more houses to be targeted in places where local area characteristics show inefficient elements, and may therefore potentially capture a greater number of households per unit of cost, compared to the existing self-referral methods. We propose a spatial method to show the extent of building envelopes, heating systems and temperature controls. Heating controls, which are not recorded in the United Kingdom Homes Energy Efficiency Database (HEED), but we believe would be considered good practice to maintain balanced temperatures around the house, and also potentially reduce the complexity in modelling the thermal zones. Additionally, heating controls are seen as compulsory in new building regulations, an eligible measure in Green Deal and Energy Company Obligations, and in the United Kingdom Department of Energy and Climate Change (DECC) heat strategy. This paper has taught us that the emerging picture surrounding local energy modelling and that, for example, singularities such as group heating and district heating (decentralised energy supply) have a great impact on final energy consumption calculations. [ABSTRACT FROM AUTHOR]

Existing data-driven models of building energy consumption have incorporated solar irradiance as a covariate without accounting for key physical constraints, leaving room for improvement in model performance and interpretability. In this paper, we propose the Time-Of-Week, Solar and Temperature (TOWST) model for quantifying building energy consumption. This new model incorporates solar gain into the well-known Time-Of-Week and Temperature (TOWT) model by accounting for important physics constraints, including, building orientation and the opposite impact of solar gain on heating and cooling. An iterative algorithm is developed to cluster the energy demand into heating and cooling categories, identify building orientation, and estimate the unknown model parameters. The model is applied to datasets generated from a building simulation according to international standard ISO 52016-1:2017, for a range of buildings types and locations. Results show a 30%–72% reduction in mean squared error relative to the TOWT model, across a range of different climatic conditions, demonstrating the significance of accounting for solar gain in a physically meaningful way. • Data-driven building energy model incorporating the physics of solar gain. • Identifies building orientation to capture the irradiance received by a building. • Infers heating and cooling hidden states to model the physical dependence. • Significant improvement in model fit across range of climatic conditions. [ABSTRACT FROM AUTHOR]

The development and utilization of solar energy, meteorological forecasting, and agricultural production all require accurate predictions of radiance. However, due to the influence of meteorological conditions and the failure of observation instruments and design defects, the data collection of all-sky radiance distribution is often missing or inaccurate. In this paper, a new modified method of all-sky radiance distribution (CASR) is proposed based on the Spatial distribution of daylight model recommended by Commission Internationale de l'Eclairage (CIE model). Firstly, a functional relationship between sky luminance and radiance was established based on measured data. Secondly, the abnormal values were divided into four categories according to the number of anomalous regions of radiance and the frequency of abnormal values in each sky region is determined, and the reasons were analyzed according to different altitude angles. Thirdly, the radiance and sky luminance anormal values were screened to determine their sky type by comparing them to a dataset of 15 standard sky types. The abnormal values were then corrected using the CIE luminance model with the relationship between radiance and sky luminance. Finally, the CASR model was validated against existing models. The results show that the CASR model accuracy was better than the existing model, which can accurately correct the sky luminance and radiance abnormal values and accurately characterize the distribution of radiance of the whole sky. It provides a theoretical basis for radiance prediction, natural lighting analysis and building energy consumption analysis. • Linear and polynomial functional relationships between radiance and sky luminance were established. • Classified and summarized the anomaly data according to their causes and the number of radiance anomaly regions. • A new modified method of all-sky radiance distribution based on the principle of photothermal integration was proposed. [ABSTRACT FROM AUTHOR]

While technology advancements are improving the energy efficiency of buildings, occupant behavior remains a critical factor in ensuring the effectiveness of such enhancements. To this end, numerous eco-feedback systems have been developed to reduce building energy use through influencing occupants' behaviors during building operations. Information representation is a critical component in eco-feedback systems, affecting the users' interpretation, engagement, and motivation to reduce energy consumption. Many studies have focused on using different charts and technical units or abstract and artistic visualizations to represent energy consumption. However, the effectiveness of such techniques varies across studies. Recent research emphasizes the need to integrate information representation strategies that balance numeric and aesthetic appeal. Concurrently, studies have called for increased adoption of a Building Information Model (BIM) during a building's operations phase to improve facility management. In this paper, we introduce a new eco-feedback information representation method that combines numeric and aesthetic appeal through leveraging spatial and color-coding techniques in BIM. The BIM-integrated energy visualization approach developed in this paper uses the Revit Application Program Interface (API) and allows users to visualize and compare energy consumption values in 2D and 3D views of a multi-family building through a color-coding scheme in an as-built BIM. The method is validated through a user survey that quantitatively and qualitatively assesses the proposed 2D and 3D BIM eco-feedback compared to more traditional bar chart based eco-feedback. Our findings suggest that 2D spatial, color-coded eco-feedback provides the optimal information representation, as it is easy to understand, while evoking engaging and motivating responses from users. This study advances our understanding of eco-feedback information representation while expanding BIM applications during building operations. These are important steps to address the human dimension of energy efficiency in the built environment. [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]

With global warming intensifying and resource conflicts escalating, the world is undergoing a transformative shift toward sustainable practices and energy-efficient solutions. With more than 32% of the global energy used by commercial and residential buildings, there is an urgent need to revisit traditional approaches to Building Energy Management (BEM). Within a BEMSplatform, regulating the operation of Heating, Ventilation, and Air Conditioning (HVAC) systems is more important, noting that HVAC systems account for about 40% of the total energy cost in the commercial sector. This paper offers a Deep Reinforcement Learning (DRL) algorithm as a data-driven approach to controlling HVAC operation to enhance the energy efficiency of commercial buildings with open offices while ensuring thermal comfort for occupants in different zones. Compared to alternative methods such as rule-based models and model-predictive control, data-driven models have shown promising results in optimizing building energy consumption without the need for building-specific thresholds, prior knowledge about the underlying physics of heat distribution, and digital mapping of the airflow. Despite the astonishing performance of modern DRL methods in controlling energy management, a particular energy-saving solution for open-plan offices with multiple Variable Air Volume (VAV) systems, where different zones cannot be treated independently, is still missing. Also, some of the existing methods suffer from key issues such as long training time and lack of generalizability for using over-complicated models, incorporating external factors that are hard to model and characterize, and including factors that are not typically accessible. To solve these issues, we propose a low-complexity DRL-based model with multi-input multi-output architecture for the HVAC energy optimization of open-plan offices, which uses only a handful of controllable and accessible factors. The efficacy of our solution is evaluated through extensive analysis of the overall energy consumption and thermal comfort levels compared to a baseline system based on the existing HVAC schedule from a real case. This comparison shows that our method achieves 37% savings in energy consumption with minimum temperature violation (< 1%) of the desired temperature range during work hours. It takes only a total of 40 min for 5 epochs (about 7.75 min per epoch) to train a network with superior performance and covering diverse conditions for its low-complexity architecture; therefore, it easily adapts to changes in the building setups, weather conditions, occupancy rate, etc. Moreover, by enforcing smoothness on the control strategy, we suppress the frequent and unpleasant on/off transitions on HVAC units to avoid occupant discomfort and potential damage to the system. The generalizability of our model is verified by applying it to different building models and under various weather conditions. [Display omitted] • Model and analyze the thermodynamics of multi-zone open-plan offices • DRL-based control algorithm to optimize thermal comfort and energy efficiency • 37% reduction in HVAC energy with less than 1% temperature violation • Smoothness-imposing reward function to minimize on-off transitions • Applicable to different floor plans under various weather conditions [ABSTRACT FROM AUTHOR]

• A BIM-supported energy performance analysis for green building design is proposed. • This systematic analytical framework integrates explainable machine learning and multi-objective optimization. • It can serve as a data-driven decision tool to guide green building development even under uncertainty. • The top important features that greatly affect the building energy performance can be quantitatively identified. • Building energy performance can be improved by 13.43% using the obtained optimal strategy. Supported by the combination of the advanced BIM technique with intelligent algorithms, this paper develops a systematic framework using explainable machine learning and multi-objective optimization to realize the automatic prediction and optimization of building energy performance towards the sustainable development goal. There are three critical parts incorporated, including the DesignBuilder simulation, BO-LGBM (Bayesian optimization-LightGBM) and an explainable method SHAP (SHapley Additive explanation)-based prediction and explanation of building energy performance, and AGE-MOEA algorithm-based multi-objective optimization (MOO) under sources of uncertainty. It has been verified in a case study for green building design. Results show that: (1) The predictive BO-LGBM model can make a highly precise prediction in agreement with the simulation data, reaching up the R2 larger than 93.4% and MAPE smaller than 2.13%. From the SHAP calculation, features related to the HAVC (Heating Ventilation and Air Conditioning) system tend to contribute more to affecting the prediction results. (2) The AGE-MOEA-based optimization can identify a set of Pareto optimal solutions in simultaneously minimizing the building energy consumption, CO 2 emission, and indoor thermal discomfort degree, arriving at the highest optimization rate of 13.43% under proper adjustment of certain features. (3) In the MOO task, the consideration of model and data uncertainty by prediction intervals and Monte-Carlo simulation can further increase the optimization rate by around 4.0% than the deterministic scenario, resulting in more desired strategies in optimizing the green building performance. In short, this paper enriches the area of green building development. For one thing, it raises the transparency and interpretability of machine learning to make the prediction more convincing. For another, it efficiently determines the passive and active design solutions along with the detailed profile of influential factors for green building design. [ABSTRACT FROM AUTHOR]

Recently, demand flexibility has been highlighted as a promising distributed resource from the customer side, especially from industrial customers like commercial buildings, capable of providing grid support services. However, the quantification of demand flexibility is a complex process that requires a methodology including the requirements of both the grid operators and the customers. This paper proposes a novel approach to quantify the available demand flexibility of individual buildings, while taking into account the underlying building energy physics. The proposed approach constructs on the operational flexibility concept from the power systems, and extends it to include a comfort domain, identifying different flexibility parameters with the aim of giving a better insight into the flexibility potential of commercial buildings. This method includes a development of building energy simulations to assess the effects of weather variations, construction types, and comfort constrains on demand flexibility. The proposed quantification method has been validated using 15 different office building models and two different climate zones, i.e., the Netherlands and Texas, US. The results presented in this paper suggest that buildings located in a hot climate could offer higher flexibility potential during shorter time ranges, while buildings in a cold climate could offer lower flexibility potential but during longer time ranges. Determining these differences could potentially facilitate the dispatch of flexible demand resources, to assess their real potential, and to schedule demand flexibility between stakeholders. [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]

Understanding the contribution a resource can make to the power system could indicate where its value lies. This paper estimates the capacity value of a load-shifting resource which is capable of providing multiple services. The capacity value represents the contribution of a resource to generation adequacy and an understanding of this contribution is important to compare how different power system resources can assist power system operators and planners. Additionally, policy-makers and market operators need an appreciation of the capacity value of different resources in order to design capacity remuneration mechanisms. A building energy model coupled with a power system model, co-optimizing the supply-side and the demand-side, is employed in this paper to estimate the capacity value of a specific load-shifting resource. The resource examined is electric thermal storage heating devices for space and water heating. Ireland is used as a test case. It was found that these load-shifting devices can provide an adequacy contribution to the power system and thus have a capacity value. The capacity value, for the Irish case, can be up to 26% for the DR resource in question for the given year but the values are typically much lower due to operational constraints (reserve provision) and due to occupancy profile impacts. The results highlight the need for holistic modeling of demand response resources, as well as the need for additional work for different load-shifting resources and more data. [ABSTRACT FROM AUTHOR]

This paper presents hybrid control approaches for heating air supply in response to changes in demand by using the Fuzzy Inference System (FIS) and Artificial Neural Network (ANN) fitting models. Since early 2000’s, some advanced computing and statistical tools were introduced to replace conventional control models in improving control and energy efficiency. Among the tools, the FIS and ANN algorithms were used to define complex interactions between inputs and outputs, and were able to facilitate control models to predict or evaluate precise thermal performance. This paper introduces the FIS and ANN control schemes for simultaneously controlling the amount of supply air and its temperature. Input and output data derived from the FIS results generate and validate the ANN model, and both models are compared to the typical thermostat on/off baseline control to evaluate conditions of supply air for a heating season. The differences between the set-point and actual room temperature and their sums indicate control efficiency, and the heat gains into a room and their sums define the energy consumption level. This paper concludes that the simultaneous control of mass and temperature maintains the desired room temperature in a highly efficient manner. Sensitive controls may have a disadvantage in terms of energy consumption, but the ANN controller can minimize energy consumption in comparison with simple thermostat on/off controller. The results also confirm the effectiveness of simultaneous control of mass and temperature using an ANN algorithm corresponding to intermittent or unpredicted changes in thermal demands. [ABSTRACT FROM AUTHOR]

This paper demonstrates the application of Autotune, a methodology aimed at automatically producing calibrated building energy models using measured data, in two case studies. In the first case, a building model is de-tuned by deliberately injecting faults into more than 60 parameters. This model was then calibrated using Autotune and its accuracy with respect to the original model was evaluated in terms of the industry-standard normalized mean bias error and coefficient of variation of root mean squared error metrics set forth in ASHRAE Guideline 14. In addition to whole-building energy consumption, outputs including lighting, plug load profiles, HVAC energy consumption, zone temperatures, and other variables were analyzed. In the second case, Autotune calibration is compared directly to experts’ manual calibration of an emulated-occupancy, full-size residential building with comparable calibration results in much less time. The paper concludes with a discussion of the key strengths and weaknesses of auto-calibration approaches. [ABSTRACT FROM AUTHOR]

This paper explores the effects of residential neighborhood types on building energy consumption and solar energy trade-offs with the goal of developing an operational methodology to achieve the best conditions towards a zero nonrenewable energy neighborhood. Six typical Chongming neighborhood types are identified, termed the Farmhouse, Linear-village, Nucleated-village, Townhouse, Slab and High-rise types with the effects of increased installation of solar energy panels estimated by Monte Carlo simulation. To achieve a more comprehensive view of cost-effective opportunities to improve the performance of buildings at the neighborhood scale, we use actual energy use data obtained from both the survey and electricity bills’ analysis to show that considerable variations exist due to hitherto unrecorded socioeconomic factors relating to the type of occupants involved. By concluding that the associated variables are interdependent, the paper continues with an empirical study in which the solar energy potential of each neighborhood is quantified to show that, in the case of Chongming, the Nucleated-village and Slab neighborhood types have the greatest retrofitting potential. These investigations into energy trade-offs with existing retrofitting and construction technologies therefore provide a metric for other eco-cities to use as a tool for future policy guidance. [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]

[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]

Air layer involved envelopes (ALIEs) have gained considerable popularity in modern building design and construction, owing to their great potential in improving the building thermal performance. Basically, the air layer functions as an extra insulation layer or as a ventilation channel. This paper presents a literature review on building envelopes that contain inner air layers by tracing recent studies on existing air layer involved applications and technologies in walls, windows, roofs. The structural characteristics, the driving forces, the effects of the inner air layers, and the benefits of different types of ALIE systems are summarized and classified. And then operation modes of air layer used in building envelopes are roughly classified into three types: the enclosed type, the naturally ventilated type and the mechanically ventilated type. At the end, this paper analyzes current research gaps and provides possible future research directions on air layer technologies in building envelopes. [ABSTRACT FROM AUTHOR]

The aim of this paper is to assess the performance of several designs of hybrid systems composed of solar thermal collectors, photovoltaic panels and natural gas internal combustion engines. The software TRNSYS 17 has been used to perform all the calculations and data processing, as well as an optimisation of the tank volumes through an add-in coupled with the GENOPT® software. The study is carried out by analysing the behaviour of the designed systems and the conventional case in five different locations of Spain with diverse climatic characteristics, evaluating the same building in all cases. Regulators, manufacturers and energy service engineers are the most interested in these results. Two major contributions in this paper are the calculations of primary energy consumption and emissions and the inclusion of a Life Cycle Cost analysis. A table which shows the order of preference regarding those criteria for each considered case study is also included. This was fulfilled in the interest of comparing between the different configurations and climatic zones so as to obtain conclusions on each of them. The study also illustrates a sensibility analysis regarding energy prices. Finally, the exhaustive literature review, the novel electricity consumption profile of the building and the illustration of the influence of the cogeneration engine working hours are also valuable outputs of this paper, developed in order to address the knowledge gap and the ongoing challenges in the field of distributed generation. [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]

Buildings account for approximately 32% of the total energy consumption globally and up to 40% in the developed countries, which makes buildings a prime target for energy conservation. Various energy conservation measures (ECMs) have been proposed to improve the energy efficiency in buildings, and these ECMs are usually designed and assessed using calibrated building energy models. However, there is empirical evidence that reveals noticeable discrepancies between simulated performances of ECMs reported in building energy models and their actual performances measured in buildings. This paper examines two possible causes of such discrepancies. Specifically, this paper tests the following two hypotheses: (1) using assumed occupancy data as opposed to actual occupancy data in building energy simulation reduces the reliability of estimated performance of demand-driven ECMs; and (2) using an energy model built for one ECM to cross estimate energy consumption of another ECM is statistically inaccurate. An educational building was used as a test bed. The results proved both hypotheses true, showing that estimations were more accurate and consistent for models calibrated using actual occupancy compared with those using assumed occupancy, and that cross-ECM estimation resulted in statistical inaccuracy. The findings indicated that current building energy modeling methods have limited reliability in ECM performance assessment, and need to be improved to better support the design and implementation of ECMs in buildings. [ABSTRACT FROM AUTHOR]

European Directive 2010/31/EU stated that by the end of 2020 all new buildings should be nearly Zero-Energy Buildings (nZEB). Since such buildings require a low quantity of energy for heating, they can utilize energy from the return line of a District Heating (DH) system. Further, this new type of buildings can successfully be integrated into the fourth generation of heat distribution technology, which is the new trend in the DH industry. On the other hand, existing building stock has a service lifetime of around 50 years, indicating that the required supply temperature in the DH system cannot be lowered beneath a certain level. Hereafter, together with new types of buildings and different policies, this could lead to changes in heat demand profiles of the DH system. The above-mentioned situation in the building market will lead to changes in heat load profiles and unavoidably influence the performance indicators of energy conversion units. Therefore, the focus in this paper was devoted to operation analysis of ethanol-based a Combined Heat and Power (CHP) plant with combined cycle technology under changeable demand conditions and different temperature levels in the DH system. In this paper different temperature levels of the DH system together with two temperature control strategies were considered. Further, the analysis included different heat demand profiles. The analysis was performed in Aspen HYSYS simulation software and data post processing was made by MATLAB. The results found that effective plant operation is highly dependent on heat load profile. Temperature control strategies did not induce a significant change in overall power production in CHP. The decrease in the supply temperature did not show a significant impact on plant performance. However, increase in temperature difference between supply and return lines led to higher power production and better overall plant performance. Further, it was concluded that it is rather difficult to operate CCPP connected to low energy building stock. This means that the CCPP is suitable for high-density heat areas, while it has poor energy performance indicators in low heat density areas. [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]

EU targets require nearly zero energy buildings (NZEB) by 2020. However few monitored examples exist of how NZEB has been achieved in practise in individual residential buildings. This paper provides an example of how a low-energy building (built in 2006), has achieved nearly zero energy heating through the addition of a solar domestic hot water and space heating system (“combi system”) with a Seasonal Thermal Energy Store (STES). The paper also presents a cumulative life cycle energy and cumulative life cycle carbon analysis for the installation based on the recorded DHW and space heating demand in addition to energy payback periods and net energy ratios. In addition, the carbon and energy analysis is carried out for four other heating system scenarios including hybrid solar thermal/PV systems in order to obtain the optimal system from a carbon efficiency perspective. [ABSTRACT FROM AUTHOR]

In two earlier papers we proposed a process assumption - based design method, one aim of which is the determination of the thermal requirement of a building by investigating the building functioning as a dynamic thermal system. The principal constraint of that determination is the building indoor temperature range to be no more than 2 °C. In this paper we focus on the thermal requirement of maximum WWR (window-to-wall ratio) allowed by the constraint as a function of envelope U -value and ambient temperature amplitude. Seven US cities are studied to represent a range of ambient temperature amplitudes. As the window part of a building’s envelope is a prominent architectural feature of the building, WWR and its allowed maximum in terms of thermal autonomy are the signature/reflection of local ambient temperature amplitude and the variety of envelopes of building stock in each locality. Such signal characteristics are otherwise referred to as regional architecture. [ABSTRACT FROM AUTHOR]

The interest in self-consumption of PV electricity from grid-connected residential systems is increasing among PV system owners and in the scientific community. Self-consumption can be defined as the share of the total PV production directly consumed by the PV system owner. With decreased subsidies for PV electricity in several countries, increased self-consumption could raise the profit of PV systems and lower the stress on the electricity distribution grid. This review paper summarizes existing research on PV self-consumption and options to improve it. Two options for increased self-consumption are included, namely energy storage and load management, also called demand side management (DSM). Most of the papers examine PV-battery systems, sometimes combined with DSM. The results show that it is possible to increase the relative self-consumption by 13–24% points with a battery storage capacity of 0.5–1 kW h per installed kW PV power and between 2% and 15% points with DSM, both compared to the original rate of self-consumption. The total number of papers is however rather limited and further research and more comparative studies are needed to give a comprehensive view of the technologies and their potential. Behavioral responses to PV self-consumption and the impact on the distribution grid also need to be further studied. [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]

Highlights: [•] This paper reviews up to date methods for building energy benchmarking. [•] This paper summarizes the major characteristics of these methods. [•] This paper recommends a flow chart for reader to choose a proper method. [Copyright &y& Elsevier]

Abstract: Thermal energy storage with phase change materials (PCMs) offers a high thermal storage density with a moderate temperature variation, and has attracted growing attention due to its important role in achieving energy conservation in buildings with thermal comfort. Various methods have been investigated by previous researchers to incorporate PCMs into the building structures, and it has been found that with the help of PCMs the indoor temperature fluctuations can be reduced significantly whilst maintaining desirable thermal comfort. This paper summarises previous works on latent thermal energy storage in building applications, covering PCMs, the impregnation methods, current building applications and their thermal performance analyses, as well as numerical simulation of buildings with PCMs. Over 100 references are included in this paper. [Copyright &y& Elsevier]

• On-demand crystallization of sodium acetate trihydrate for heat storage. • A numerical model was validated with data from system demonstration. • Sensitivity analysis of solar collector area and heat storage volume. • Annual solar fraction of 71% with 0.6 m3 water and 1 m3 of PCM for heat storage. • 1000 kWh of heat supplied by PCM units with 5.5 annual heat storage cycles. To reduce the energy consumption of buildings significantly, a novel solar combi-system with short and long-term heat storage has been developed. A system prototype with 22.4 m2 (aperture) evacuated tubular collectors, a 735 L water tank and 4 phase change material (PCM) units each containing 150 L sodium acetate trihydrate composite has been built. Experimental investigation has shown advantages of utilization of stable supercooling of sodium acetate trihydrate in spring and autumn. In this paper, a newly developed numerical model was used to investigate the performance potential of the system with combined utilization of the water tank and the PCM units, including on-demand crystallization of supercooled sodium acetate trihydrate composites. PCM units, the water tank and the collector circuit models were validated with measurement data from system demonstration. Space heating and hot water demand patterns of a Danish single-family Passive House with a yearly heat demand of 3723 kWh were applied. Results showed that a 56% annual solar fraction of heat supply was achieved with the prototype specifications. A 69% solar fraction could be achieved with an optimized scenario including a 15% increased hot water demand. Sensitivity analysis of component sizing showed that PCM units of 200 L can be more efficiently used with a 0.6 m3 water tank. Optimal solar collector array tilt was 70°. Aperture areas between 12.8 and 22.4 m2 were found adequate for frequent utilization of a PCM volume up to 1 m3. Thus, the PCM heat storage capacity could be utilized at least 5.5 times a year. With a 22.4 m2 collector area and 5 PCM units of 200 L each, a solar fraction of 71% was calculated for the annual heat supply. Assuming full charge of a 0.6 m3 water tank and 2.8 m3 of sodium acetate trihydrate composite by electricity at the beginning of the year, the system could run 18 days without need for auxiliary heating. Thus, in periods without solar collector power available, generation maxima of wind power could be utilized. In conclusion, building heat demand could be covered close to 100% by renewable energy resources. [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]

• Energy prediction model has been combined with estimated occupancy profile. • Blind system identification models give accurate estimation of occupancy. • Extra input of occupancy can improve the accuracy of electricity prediction models. • Best performance is obtained with ensemble model in all neural network structures. Occupancy behaviour plays an important role in energy consumption in buildings. Currently, the shallow understanding of occupancy has led to a considerable performance gap between predicted and measured energy use. This paper presents an approach to estimate the occupancy based on blind system identification (BSI), and a prediction model of electricity consumption by an air-conditioning system is developed and reported based on an artificial neural network with the BSI estimation of the number of occupants as an input. This starts from the identification of indoor C O 2 dynamics derived from the mass-conservation law and venting levels. The unknown parameters, including the occupancy and model parameters, are estimated by using a frequentist maximum-likelihood algorithm and Bayesian estimation. The second phase is to establish the prediction model of the electricity consumption of the air-conditioning system by using a feed-forward neural network (FFNN) and extreme learning machine (ELM), as well as ensemble models. To analyse some aspects of the benchmark test for identifying the effect of structure parameters and input-selection alternatives, three studies are conducted on (1) the effect of predictor selection based on principal component analysis, (2) the effect of the estimated occupancy as the supplementary input, and (3) the effect of the neural network ensemble. The result shows that the occupancy number, as the input, is able to improve the accuracy in predicting energy consumption using a neural-network model. [ABSTRACT FROM AUTHOR]

Highlights • A probabilistic load forecasting model for buildings is developed. • Weather forecasting uncertainty is quantified and used for load forecasting. • Uncertain peak load is modeled considering its magnitude and occurrence. • A hybrid method is proposed to decompose the original load dataset. • Case studies are conducted to compare different means of using weather forecasts. Abstract This paper presents a new probabilistic building load forecasting model considering uncertainties in weather forecasts and abnormal peak load. Two basis function components are developed, including the probabilistic normal load forecasting and the probabilistic uncertain peak load (or abnormal peak load) forecasting. The probabilistic normal load forecasting model is built using the artificial neural network (ANN) and the probabilistic temperature forecasts. The probabilistic abnormal peak load forecasting model consists of two models quantifying the probabilistic occurrence and magnitude of the peak abnormal differential load respectively. The test results show that the ANN deterministic load forecasting model can achieve satisfactory performance with the average mean absolute percentage error (MAPE) of 5.0%. The probabilistic occurrence model can forecast the occurrence frequency of the peak abnormal differential load with the satisfactory agreement, and the probabilistic magnitude model can well forecast the magnitudes of the peak abnormal differential load with the Kolmogorov-Smirnov error of 0.09. Real-time application case studies are conducted by different means of using the probabilistic weather forecasts. The results show that the probabilistic normal load forecasts have satisfactory accuracies and the load forecasts based on the one-day-ahead probabilistic weather forecasts are the best. [ABSTRACT FROM AUTHOR]

Highlights • The natural ventilation reduction varies from 5% to 70% across the US. • Most cities have less than 20% reduction except for four cities in the US. • PM2.5 is the most significant outdoor air pollutant compared to PM10 and Ozone. • We have developed an advanced indoor air pollutant control with emulators. Abstract Natural ventilation has emerged as a desirable feature of sustainable buildings given its potential positive impact on building energy performance and amenities for occupants. In some circumstances, natural ventilation can provide a higher ventilation rate compared to mechanical ventilation, thus improving the air quality of indoor space, resulting in lower indoor carbon dioxide and volatile organic compound concentrations. However, this increased ventilation rate also raises the issue of increased indoor pollutant concentration from outdoor sources, which has been proven to significantly affect occupant health. In this paper, we investigate the influence of three major outdoor air pollutants, PM2.5, PM10, and ozone, on natural ventilation usage across 12 major US cities and their corresponding climate zones utilizing the outdoor air pollutant records from the US Environmental Protection Agency. Firstly, a descriptive statistics study presents a general description of air pollutant records in these investigated cities and climate zones. Then two natural ventilation operation scenarios (considering outdoor air pollutants vs. not) are developed and compared to explicitly show the expected natural ventilation reduction in these areas. The investigation results indicate that the most polluted area for natural ventilation is Los Angeles (with a projected 70% reduction), followed by Chicago (approximately 40% reduction), and then Atlanta and San Francisco (20–30% reduction for each). The natural ventilation reduction caused by outdoor air pollutants ranges from 5% to 20% in all other tested cities. Among the three pollutants (PM2.5, PM10, and ozone) we investigated, the influence of PM2.5 consistently emerges as the most critical to consider, while the impact of PM10 is typically trivial. The influence of ozone is not obvious in most cases. Nevertheless, in certain cases, its influence is non-negligible when natural ventilation is utilized. This study aims to provide a general guideline for decision makers to consider the influence of outdoor air quality on natural ventilation usage when adopting natural ventilation in different US locations. The results also confirmed the outdoor air pollutants, especially PM2.5, as a significant factor to consider in the natural ventilation design to shield the occupant from excessive air pollutant exposure. [ABSTRACT FROM AUTHOR]

Highlights • Improve nearly zero energy building performances at community level by collaborations. • Develop a grouping method that can maximize the collaboration benefits. • Identify representative energy characteristics by clustering algorithm. • Study performance improvements in economic cost and grid interactions. Abstract Collaborations among nearly zero energy buildings (nZEBs) (e.g. renewable energy sharing) can improve nZEBs' performance at the community level. To enable such collaborations, the nZEBs need to be properly grouped. Grouping nZEBs with similar energy characteristics merely brings limited benefits due to limited collaboration existed, while grouping nZEBs with diverse energy characteristics can bring more benefits. In the planning of nZEB communities, due to the large diversity of energy characteristics and computation complexity, proper grouping that maximizes the collaboration benefits is difficult, and such a grouping method is still lacking. Therefore, this paper proposes a clustering based grouping method to improve nZEB performance. Using the field data, the grouping method first identifies the representative energy characteristics by advanced clustering algorithms. Then, it searches the optimal grouping alternative of these representative profiles that has the optimal performance. For validation, the proposed grouping method is compared with two cases (the nZEBs are either not grouped or randomly grouped) in aspects of economic costs and grid interaction. The study results demonstrate that the proposed method can effectively improve nZEBs' performances at the community level. The propose method can provide the decision makers a means to group nZEBs, which maximize the collaboration benefits and thus assists the planning of nZEB communities. [ABSTRACT FROM AUTHOR]

Highlights • Create city-specific energy performance grading model using city disclosure data. • Method to grade buildings that is interpretable, reproducible, scalable, and robust. • Develop a clustering algorithm to grade buildings on performance. • Validated approach demonstrates significant improvements over current methods. Abstract As the effects of anthropogenic climate change become more pronounced, local and federal governments are turning towards more aggressive policies to reduce energy use in existing buildings, a major global contributor of carbon emissions. Recently, several cities have enacted laws mandating owners of large buildings to publicly display an energy efficiency rating for their properties. While such transparency is necessary for market-driven energy reduction policies, the reliance on public-facing energy efficiency grades raises non-trivial questions about the robustness and reliability of methods used to measure and benchmark the energy performance of existing buildings. In this paper, we develop a building energy performance grading methodology using machine learning and city-specific energy use and building data. Leveraging the growing availability of data from city energy disclosure ordinances, we develop the GREEN grading system: a framework to facilitate more accurate, fair, and contextualized building energy benchmarks that account for variations in the expected and actual performance of individual buildings. When applied to approximately 7500 residential properties in New York City, our approach accounts for the differential impact of design, occupancy, use, and systems on energy performance, out-performing existing state-of-the-art methods. Our model and findings reinforce the need for more robust, localized approaches to building energy performance grading that can serve as the basis for data-driven urban energy efficiency and carbon reeduction policies. [ABSTRACT FROM AUTHOR]

Highlights • Overview of US Federal Energy Management (FEMP) lighting efficiency program. • Estimated total benefits of the 2017 lighting update from $33 to 217 million USD. • Estimated energy savings 0.33–4.76 TWh for this update. • Total benefits methodology with monetized full fuel cycle greenhouse gas emissions. • Presents the FEMP lighting categorization structure. Abstract Lighting consumes a great deal of electrical energy in buildings, and the U.S. federal government is the largest single energy consumer in the United States. Therefore, the way that the U.S. federal government manages its lighting energy use is of great importance. One important way that energy use is managed is through Federal Energy Management Program (FEMP) energy efficiency requirements. However, there is not much literature on how these requirements are set and how to value the resulting savings. This paper documents the methodology used to develop these for the 2017 FEMP lighting update. A methodology for calculating the total benefits is presented that goes beyond site energy saved, and includes full fuel cycle energy savings, energy cost savings, emissions offset and monetized emissions costs. The results show total benefits of $33 million USD including 475 kilotons of carbon dioxide (CO 2) offset under the low compliance scenario, $104 million USD including 1494 kilotons of CO 2 offset under the medium compliance scenario, and $217 million USD including 3124 kilotons of CO 2 offset under the high compliance scenario. [ABSTRACT FROM AUTHOR]

Highlights • Energy consumption in higher education institutions (HEIs) modelled. • For the first time, panel data used, with cross-sectional and time-series variations. • Over time and across HEIs, energy use increases as income and size increase. • Economies of scale as HEIs grow, but energy use still rising with sector growth. • Research universities more energy intensive. Abstract Among the various sustainability goals of higher education institutions (HEIs), reducing energy use and carbon emissions are particularly important. However, not much is known about energy demand from the higher education sector – especially since there is a lack of robust models of energy demand in this sector. This paper, the first to utilize a panel dataset and advanced panel econometric techniques in order to model energy use in higher education, investigates variations in energy use between HEIs (cross-sectional analysis), and also changes in energy use over time (temporal analysis), using the UK as a case study. We argue that panel dataset and methods are more useful for understanding growth (and reduction) in energy use within the HE sector than the methods used within previous cross-sectional studies. Results show that, over time and also across the sector, energy consumption in the HEIs increases with increases in income and floor space, but at a slower rate. As HEIs grow overall (in terms of income, floor space, student and staff number) over time, they become more 'energy efficient' (using less energy per unit of area, population or income), indicating economies of scale in the temporal dimension. Results also show that after controlling for income and size, research intensive HEIs consume more energy. We also find a small but statistically significant effect of energy prices on energy consumption, as might be expected. Simulation using the model parameters for an example scenario suggests that energy consumption will continue to increase unless there is a significant change in the policies driving income growth and spatial expansion in the HE sector in the UK. [ABSTRACT FROM AUTHOR]

Highlights • Novel energy flexible building based on smart demand-side management. • Integration of heat pump, latent heat storage and smart demand response. • Smart integration able to operate at optimal times for electricity grid and consumers. • Economic and environmental benefits are achieved. • Energy flexible buildings can have a positive impact on the heating decarbonisation. Abstract One of the greatest challenges for long-term emissions reduction is the decarbonisation of heating and cooling due to the large scale, seasonal variation and distributed nature. Energy flexible buildings with electric heating, smart demand-side management and efficient thermal energy storage are one of the most promising strategies to deploy low-carbon technologies which can benefit the electricity system by reducing the need of reinforcing existing networks and their ability to use electricity in times of low demand and high supply. Combined with spot price contracts, in which the electricity tariff changes every half-hour depending on supply and demand, they can effectively reduce on-peak demand periods, achieve economic profits for end-users and retailers, and reduce the environmental impact of the electricity grid by operating in periods with lower CO 2 emissions rate. To achieve these benefits, it is crucial to develop accurate models for energy flexible buildings as well as control strategies to optimise the complex system operation. This paper proposes a novel flexible energy building concept, based on smart control, high density latent heat storage and smart grids, able to predict the best operational strategy according to the environmental conditions, economic rates and expected occupancy patterns. The smart integration model, carried out in TRNSYS for a Scottish case study, solves a multi-criteria assessment based on future energy demand prediction (learning machine model supported by end-user's predefined occupancy by Internet of Things, present and forecast weather data, and building load monitoring), electricity tariff evolution and building performance. The results show that end-user's electricity bill savings of 20% are obtained and retailer's associated electricity cost is reduced by 25%. In addition, despite an increase in final energy consumption of up to 8%, the environmental impact remains constant due to operation at times with lower CO 2 emissions rate in electricity generation. The developed tools enable the design of smart energy systems for energy flexible buildings which can have a large positive impact on the building sector decarbonisation. [ABSTRACT FROM AUTHOR]

Aiming to utilize the flexibility of smart buildings for flexible operation of active distribution network, a combined modeling and optimal scheduling method for the active distribution network with integrated smart buildings is proposed in this paper. Based on the heat storage characteristics of a building, the energy consumption prediction model of the building considering different heating zones with different orientations is developed using the Resistor-Capacitor thermal network model. Then, different optimal control methods of the heating, ventilation and air-conditioning system in the building are developed. The energy consumption management of the heating, ventilation and air-conditioning system is achieved by adjusting the room temperature within the suitable temperature comfort range. In order to further consider the impact of the integration of smart buildings on the economic and security operation of the active distribution network, the optimal scheduling method of the active distribution network with integrated smart buildings is developed considering the load factor of the aggregation of the smart buildings. Finally, the optimal scheduling results of the aggregation of the smart buildings under different heating, ventilation and air-conditioning control methods in the winter heating scenario are analyzed. In addition, based on the branch flow model, the optimal power flow model of active distribution network with on-load tap changer is constructed by piecewise linearization and second-order cone relaxation to achieve flexible and optimal operation of the active distribution network. Thus, the impacts of the optimal schedules of the aggregation of smart buildings on the economic and security operation of the active distribution network are further evaluated. Numerical studies demonstrate that the proposed optimal scheduling method can make full use of the demand response potential of the smart buildings and further contribute to the operating costs reduction of the smart buildings. Meanwhile, the optimization of the active distribution network with the load factor of the aggregation of buildings can reduce the power loss and increase the minimum voltage magnitude of the active distribution network utilizing the flexibility of the smart buildings. [ABSTRACT FROM AUTHOR]

Buildings account for 32% of worldwide energy usage. A new regime of exciting new “applications” that span a distributed fabric of sensors, actuators and humans has emerged to improve building energy efficiency and operations management. These applications leverage the technological advances in embedded sensing, processing, networking and methods by which they can be coupled with supervisory control and data acquisition systems deployed in modern buildings and with users on mobile wireless platforms. There are, however, several technical challenges to confront before such a vision of smart building applications and cyber-physical systems can be realized. The sensory data produced by these systems need significant curation before it can be used meaningfully. This is largely a manual, cost-prohibitive task and hence such solutions rarely experience widespread adoption due to the lack of a common descriptive schema. Recent attempts have sought to address this through data standards and metadata schemata but fall short in capturing the richness of relationships required by applications. This paper describes Brick , a uniform metadata schema for representing buildings that builds upon recent advances in the area. Our schema defines a concrete ontology for sensors, subsystems and the relationships between them, which enables portable applications. We demonstrate the completeness and effectiveness of Brick by using it to represent the entire vendor-specific sensor metadata of six diverse buildings across different campuses, comprising 17,700 data points, and running eight unmodified energy efficiency applications on these buildings. [ABSTRACT FROM AUTHOR]

The large penetration rate of renewable energy sources leads to challenges in planning and controlling the energy production, transmission, and distribution in power systems. A potential solution is found in a paradigm shift from traditional supply control to demand control. To address such changes, a first step lays in a formal and robust characterization of the energy flexibility on the demand side. The most common way to characterize the energy flexibility is by considering it as a static function at every time instant. The validity of this approach is questionable because energy-based systems are never at steady-state. Therefore, in this paper, a novel methodology to characterize the energy flexibility as a dynamic function is proposed, which is titled as the Flexibility Function . The Flexibility Function brings new possibilities for enabling the grid operators or other operators to control the demand through the use of penalty signals (e.g., price, CO 2 , etc.). For instance, CO 2 -based controllers can be used to accelerate the transition to a fossil-free society. Contrary to previous static approaches to quantify Energy Flexibility, the dynamic nature of the Flexibility Function enables a Flexibility Index, which describes to which extent a building is able to respond to the grid’s need for flexibility. In order to validate the proposed methodologies, a case study is presented, demonstrating how different Flexibility Functions enable the utilization of the flexibility in different types of buildings, which are integrated with renewable energies. [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]

• 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 data-driven method is a promising way to predict the energy consumption of buildings, however suffering from the data shortage problem in various scenarios. Even though transfer learning can improve the few-shot prediction performance by utilizing other buildings' data, the centralized approach poses potential privacy risks. To tackle this issue, the paper proposes a privacy-preserving knowledge sharing framework to facilitate the few-shot building energy prediction based on federated learning. First, a private data aggregation scheme is established to encrypt the sensitive data with shared random masks and guarantee the privacy of the data preprocessing and model optimization. Then, to alleviate the intrinsic data heterogeneity, a dynamical clustering federated learning algorithm is proposed to implement the intra-cluster and inter-cluster knowledge sharing along with the iterative clustering process for participating buildings. Finally, the network-based transfer learning approach is incorporated into the distributed framework to establish the customized model based on trained cluster models and further boost the prediction performance for each building. Extensive experiments on the Building Data Genome Project 2 (BDGP2) dataset indicate that the federated approach witnesses a desirable prediction performance while preserving the privacy of building occupants. • Propose a privacy-preserving knowledge sharing method for building energy prediction. • Establish a private data aggregation scheme for the sensitive information. • Develop a dynamical clustering federated learning algorithm for knowledge sharing. • Integrate transfer learning into the framework to build customized prediction models. [ABSTRACT FROM AUTHOR]

Uncertainty introduces significant complexity to the design process of distributed energy systems (DES) and introduces the risk of suboptimal decisions when the design is performed deterministically. Therefore, it is important that computational DES design models are able to account for the most relevant uncertainty sources when identifying optimal DES configurations. In this paper, a model for optimal DES design under uncertainty is presented and is formulated as a Two-stage Stochastic Mixed-Integer Linear Program. As uncertain parameters, energy carrier prices and emission factors, building heating and electricity demands, and incoming solar radiation patterns are considered and probabilistic scenarios are used to describe their uncertainty. The model seeks to make cost-optimal DES design decisions (technology selection and sizing) before these uncertain parameters are known, while it also identifies the optimal operation of the selected DES configuration for multiple uncertain scenarios. Moreover, two strategies for emission reduction are employed that set CO 2 limits either to the system’s average emissions under uncertainty (‘neutral’ strategy) or individually to the system’s emissions for every possible uncertainty outcome to ensure a more robust emission performance (‘aggressive’ strategy). To illustrate the model’s application, the design of a DES for a Swiss urban neighbourhood of 10 buildings is investigated. Multiple optimal DES configurations are obtained by using the ‘neutral’ and ‘aggressive’ emission reduction strategies and the trade-offs between the systems’ economic and emission performance are analysed. Moreover, the optimal DES are contrasted in terms of technology selection and energy consumption shares among fossil fuels, grid electricity and renewable energy. Finally, all model outputs are compared to results obtained from a deterministic design model. The comparison showed that the deterministic model leads to underestimations of the system costs and inaccurate estimates of the system’s CO 2 emissions. Moreover, the deterministic designs, in many cases, underestimate the renewable energy capacity that is required to meet the imposed CO 2 limits. These significant differences between the stochastic and the deterministic model results can serve to confirm the shortfalls of deterministic design. [ABSTRACT FROM AUTHOR]