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[Display omitted] • The state of art of wet substrate materials is presented. • Five materials were tested and compared to Kraft paper as wet substrate. • Absorption, evaporation and moisture-wicking capacities were determined according to norm NF ISO 20158, NF EN ISO 9073-6 and AATCC 198. • Taking into account the cost, our results suggest that Fibre fabrics (tight straight weave) is a good choice for evaporative cooling Pad purpose. Due to the ever-growing demand for air-conditioning to bring the indoor air temperature to a comfortable level despite the unreasonable electricity consumption, research is more oriented towards techniques in connection with new methods enabling more energy savings and less adverse environmental impacts. Dew point evaporative cooling systems hold among the most promising. Their ability to use the evaporation of water to absorb heat from the air in order to lower its temperature below its wet bulb level without adding humidity, is an extremely low-energy and eco-friendly cooling principle. The properties of the material used in the construction of the wet channel surface in an evaporative cooler, i.e, its moisture wicking ability, diffusivity and evaporation capacity, can alter cooling efficiency and performance considerably. The irregular fibres help to divert moisture and enlarge the wet area, thus promoting evaporation. A measuring device was mounted to test the capillary rise of evaporative fabrics, which is one of the techniques used to assess the liquid moisture transmission performance of fabrics. A variety of materials (fabrics) woven from different types of fibres have been experimentally tested and compared to Kraft paper, which is commonly used as a wet surface medium in evaporative cooling systems. The wicking rate of fabrics obtained from the test device correlated well with the NF EN ISO 9073-6. Consequently, the test device clearly demonstrated differences between the fabrics used in the study, and can be used to determine vertical wicking behaviour of fabrics. Most textile fabrics have been found to have superior properties and qualities in terms of moisture wicking ability than the Kraft Paper. Also, that the type of weaving and the compactness of the fabric improved the capillary rise. Compared to Kraft paper, the absorbency of some fabrics has been found 160% to 355% higher. An estimation evaluation regarding both moistures transfer and mechanical properties revealed that two of the fabrics were the most suitable for evaporative cooling applications. [ABSTRACT FROM AUTHOR]

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

The accuracy of power load forecasting plays an important role in the development of the economy and the promotion of energy consumption and transformation. Aiming at the strong fluctuation and variability mechanism of electricity consumption, this paper proposes a novel hybrid method with the Long Short-term Memory (LSTM) on the dual optimization of particle swarm algorithm and butterfly algorithm, namely the EWT-MOLSTM-SVR model. Adaptive signal decomposition Empirical Wavelet Transform (EWT) decomposes the fluctuating electricity data into different modes, and uses the frequency domain analysis of signal analysis to classify and forecast. LSTM alone reveals that the advantage of the data variability mechanism is not significant. Particle Swarm Optimization (PSO) is used for law control, and Butterfly Optimization Algorithm (BOA) is used for parameter promotion to jointly optimize the expression effect. The PSO optimizes the LSTM by improving the convergence speed and global search ability of the BOA, and the optimal hyperparameters could be searched for forecast so that the accuracy of the forecast was improved. This paper adopts the data of American residential electricity data for empirical analysis. The experimental results show that the model is significantly better than Recurrent Neural Network (RNN), General Regression Neural Network (GRNN), PSO-SVM, and other models, which has a good effect on improving the forecast accuracy. The hybrid model reveals the fluctuation mechanism and variability of electricity data, deeply analyzes the economic behavior of electricity, and provides a new method for exploring residential energy consumption and optimizing residential buildings. [ABSTRACT FROM AUTHOR]

[Display omitted] • Regions where feed-in tariff higher than valley price should follow dual objective optimization. • The economy of the household PV-BES-EV system mainly depends on the purchase cost of EVs. • Decreasing electricity price will not undermine the effectiveness of the household PV-BES-EV system. • The energy bills and carbon emissions decrease significantly with the optimal scheduling schemes. The household PV-BES-EV system (photovoltaic - battery energy storage - electric vehicle system) connected to the grid can guarantee the electricity demand of the household and facilitate the flexible trade with the grid to achieve the goals of cutting energy bills and reducing carbon emissions. In related issues, past studies often assume that renewable energy is both economical and environmentally friendly, and thus regarded it as the primary power source. While in fact, in some regions where the feed-in tariff is higher than the sales price at a certain time, households may be driven by arbitrage opportunities to frequently purchase electricity from the grid at the certain time to gain benefits, resulting in far greater carbon emissions than expected within the household measurement boundary. To avoid the environmental burden caused by pure economic incentives and to provide practical and sustainable solutions consistent with various energy price policies, the optimal scheduling schemes of the household PV-BES-EV system that considers both economic and environmental incentives for different scenarios solved by the genetic algorithm is proposed in this paper. Household data in Shanghai, China and Hamburg, Germany were selected for modelling, representing the dual-objective optimization scenario with arbitrage opportunities and the single-objective optimization scenario with sales price higher than feed-in tariff, respectively. Results show that for urban and rural residents in Shanghai, the energy bill and carbon emission would decline about 105%, 48% and 88%, 62%, respectively. As for Hamburg, the accordingly results would be 69% and 64%. In addition, based on the cost-effectiveness of the system in long-term, this paper puts forward policy recommendations that align with local conditions to further improve the economy of the system. [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]

• A new hybrid model, namely CEEMDAN-SE-LSTM is proposed. • The model allows for more accurate forecasting of electrical loads. • The model is significantly better than models like EEMD-LSTM, CEEMDAN-LSTM, etc. Ultra-short-term power load forecasting refers to the use of load and weather information from the prior few hours to forecast the load for the next hour, which is very important for power dispatch and the power spot market establishment. Based on time series decomposition-reconstruction modeling and neural network forecasting, this study constructed a CEEMDAN- 1 1 May 13, 2014, was chosen as the starting time because the meteorological data (e.g., temperature, rainfall, wind speed, etc.) obtained by the authors for Changsha City started from that time; and to highlight the cyclical nature of the electricity load demand, given the lack of meteorological data afterApril 2018, May 13, 2017, was chosen as the cut-off date in this paper. However, we believe that the starting and ending time does not affect the idea and method of model construction in this paper. SE-LSTM model and used it to forecast the ultra-short-term electricity load in Changsha, China, considering meteorological and holiday factors. The article first decomposed the power load data from May 13, 2014, to May 13, 2017, at 24 time points per day for three years to obtain six component series, and then reconstructed them into a two-component series based on the sample entropy analysis to reflect the fluctuation and trend characteristics of the power load. Then, the LSTM neural network model was used to predict and superimpose the reconstructed component series to obtain the final prediction results. It was found that the RMSE, MAE, and MAPE of the CEEMDAN-SE-LSTM model were 62.102, 47.490, and 1.649 %, respectively, which were significantly better than those of the ARMA, LSTM single-prediction, EEMD-LSTM, and CEEMDAN-LSTM models. This study greatly improves the accuracy of ultra-short-term power-load forecasting, provides support for ultra-short-term power dispatching in Changsha, and provides a reference for other cities to develop short-term and ultra-short-term power load forecasting models. [ABSTRACT FROM AUTHOR]

Energy efficiency standards for household appliances have been adopted in many countries to mitigate negative externalities, reduce emissions, and save energy. Using Chinese provincial-level panel data from 2008 to 2018, this paper builds a difference-in-difference model to examine the mechanism by which energy-efficiency upgrades in China's air conditioners affect urban household electricity usage. The results show that the upgrades have brought about an obvious reverse effect, with urban household electricity usage increasing rather than decreasing. Reducing the unit costs of energy service has enhanced residents' sensitivity to cooling demand. Driven by the substitution and income effects, urban residents may increase the intensity and frequency of their AC use. Additionally, this reverse effect is heterogeneously dependent on the family size and urban residents' age structure rather than their educational level. When families are larger and have more children, the reverse effect is more pronounced. This paper's findings provide useful recommendations for policy-makers to formulate policies that are synergistic with energy efficiency standards for household appliances. [ABSTRACT FROM AUTHOR]

• Propose a seq2point non-intrusive conforming decomposition model with a deep feature-guided attention mechanism. • Variable sliding windows are used to obtain strongly correlated power series according to the operating cycles of different appliances. • Using word embedding technique to transform regression problem into multi-classification problem to improve the accuracy of power decomposition. • Snapshot ensemble techniques are used to further improve the generalization ability and accuracy of the model. Non-intrusive load monitoring (NILM), as an important part of intelligent electricity consumption, improves the cognitive level of the load by analyzing the bus power in a specific range to obtain refined electricity consumption data at appliance level in that range, such as the number of loads, load categories, load operating status and corresponding energy consumption. For a long time, the research on NILM algorithm has been quite in depth, especially with the deep learning method being used in NILM, breakthrough progress has been made. In this paper, an integrated policy non-intrusive load monitoring technique based entirely on deep feature-guided attention mechanism is constructed based on sequence-to-point deep neural networks. Firstly, according to the operating cycle characteristics of different appliances, the data are segmented with different sliding windows and the integerized power values are mapped to high-dimensional feature vectors by word embedding, which transforms the value regression problem of decomposed power into a multiclassification problem to find the probability of decomposed power values at each discrete integer power value. Then the encoder composed of deep residual network is used for multi-layer feature extraction, which is used to obtain richer feature information, and the extracted feature quantities will be sent to the decoder through two paths respectively, one is mapped to the power feature space through word embedding as the input to the decoder; the other is sent to the decoder based entirely on the attention mechanism through convolution as the checked vector K and the content vector V. The decoder obtains the information with the highest relevance to the current moment by assigning weights to the before and after information, and thus decodes the final load decomposition result. Finally, this paper introduces a snapshot technique to obtain an inexpensive multi-model integration strategy, which improves the generalization ability of the model and the accuracy of the load decomposition. The effectiveness of the proposed method is verified by experiments on the REDD dataset and UK-DALE dataset. [ABSTRACT FROM AUTHOR]

Energy consumption of lift systems is one key issue in modern high-rise buildings, drawing property owners' attention and accounting for 6% of total electricity consumption by end-use in Hong Kong. A Benchmarking Energy Performance Indicator (BEPI), , measured in J.kg−1.m−1 and proposed by the first author of this paper for lift installations is mentioned in the Technical Guidelines of the Building Energy Code , as one emerging good engineering practice. BEPI is a fair parameter for comparing energy performance of lifts irrespective of their brand, model, travel, speed, capacity and control. It considers energy performance due to both the motor drive efficiency and traffic control intelligence. However, one main obstacle to make it statutory is the lacking of a generic monitor able to retrieve three sets of real time data (normally unavailable on an existing lift) to compute the BEPI, namely energy consumption, instantaneous car load and traveling distance, and to collect ample data statistically reliable enough to determine an acceptable threshold value. This paper describes the development of a proof-of-concept prototype BEPI monitor by giving full technical details on the rationale and history of development, the hardware components, software framework, and updated numerical results of one implementation inside the headquarters of Electrical & Mechanical Services Department (EMSD) of HKSAR. Experiences in installing and calibrating those external sensors that make the whole system non-disturbing to normal lift operation, as well as the procedures in computing the BEPI, are discussed in greater details. It has been shown that the BEPI is traffic dependent but the weekly average of it is relatively stable, confirming it may be robust enough for benchmarking purpose. The solution described in this paper may help roll out the monitoring of BEPI for all lifts inside and outside Hong Kong. [ABSTRACT FROM AUTHOR]

Understanding the demand flexibility of city-scale buildings under different demand response (DR) strategies is crucial to balance the power supply and demand in the grid. This paper presented a case study to evaluate the electricity consumption reduction potential for buildings in Changsha under different DR strategies. First, sixty-six prototype energy models for twenty-two building types of three vintages were selected to represent 59,332 buildings in Changsha. The total electricity consumption of the building stock was obtained by aggregating the prototype electricity use intensity with their representative building area. Three DR strategies were formulated, including temperature reset, lighting control, and plug load control. Second, a toolkit, AutoBPS-DR, was developed to automatically integrate individual DR strategy as well as their combination into the baseline energy models. Five scenarios were analyzed, including the baseline case, three cases with a single DR strategy, and a case with three DR strategies combined. The results showed that compared to the baseline scenario, the electricity consumption reductions of the case buildings for a 3-h DR event under temperature reset, lighting control, plug load control, and combined control scenarios were 603.97 MWh (7.94%), 441.08 MWh (5.80%), 663.26 MWh (8.72%) and 1673.32 MWh (22.00%), respectively. In the plug load control scenarios, high-rise apartments made the largest contribution with a reduction of 134.15 MWh. Medium office buildings had the most reduction under the other three scenarios, with a reduction of 122.16 MWh for temperature reset, 107.90 MWh for lighting control, and 357.16 MWh for combination control. This study demonstrated a significant DR potential for buildings in Changsha to overcome the electricity shortage period. [ABSTRACT FROM AUTHOR]

With rising energy prices, it is becoming increasingly attractive for households to form an energy community (EC) and become an active member of the energy system. Therefore, the choice of the right sharing coefficient, which defines how the produced energy is shared, is important for the expected profits. The aim of this paper is to analyse and compare two existing sharing coefficients with two new sharing coefficients. Therefore, we define two EC setups, one with only residential members and one with residential and commercial members, and investigate the impact of the sharing coefficients by using a data driven simulation model. For the analysis, we use a Monte Carlo approach in combination with load profiles of EC members simulated via time series models to account for random variation in electricity demand. The applied methodology gives additional insights on the profitability and the distribution of savings. The analysis shows that sharing coefficients that benefit small consumers are essential to incentivise private members to form an EC with commercial members. It is shown that when such an EC is formed, higher savings, a higher degree of self-sufficiency and a more efficient use of the produced electricity can be achieved. [ABSTRACT FROM AUTHOR]

• ESS degradation cost based on battery lifetime is considered for power management of nanogrid with PV system. • Multi-objective optimization representing power management determines individual power consumption of schedulable electric appliances and ESS charging/discharging. • P2P power trading is performed between nanogrids based on cooperative game model. • ESS degradation cost is decreased by 32.75% by the proposed power management. This study presents a peer-to-peer (P2P) trading strategy for power management of nanogrids with renewable energy sources (RESs), taking battery lifetime of energy storage system (ESS) into consideration. Nanogrid requires more analytical power management as individual control of each electric appliance should be carried out, considering its rated power. With the increasing penetration of distributed energy resources, P2P trading can be applied to nanogrids equipped with RESs and ESSs. For the power management of a nanogrid, multi-objective optimization is used to minimize grid power consumption, electricity cost, and delay of using electric appliances in appliance scheduling. In this paper, a battery aging model or degradation cost model is considered in the multi-objective optimization to prolong the ESS lifetime. The electricity from RESs and ESSs used for P2P trading is determined to increase the profitability of nanogrids. Depending on the amount of electricity allocated to P2P trading, a nanogrid can be a seller or buyer in P2P trading. P2P trading is carried out based on the cooperative game theory to maximize the welfare of both sellers and buyers. As compared to power management involving P2P trading without considering ESS lifetime, the proposed one in consideration of ESS lifetime decreases the electricity cost of nanogrid by 2.86% on average, and the degradation cost of ESS by 32.75% on average. [ABSTRACT FROM AUTHOR]

Decarbonization goals in the United States electricity sector are increasing the levels of renewable energy generation in the electricity supply system, and are driving increased attention to building electrification, which will increase the magnitude and shift the timing of the electricity system peak. These changes are motivating new approaches to coordinate building electricity demand with low-carbon renewable generation, elevating the importance of demand flexibility (DF) in buildings and the need to quantify the temporal impacts of DF. In this paper, we first characterize the hourly predictive accuracy of six commonly used baseline models in an application context of quantifying building-level load shift. Our analysis revealed insights such as hours of the day (afternoons), periods of the week (weekends), and seasons (summer) that were predicted with more accuracy than other time periods. In addition, the analysis showed tendencies toward overprediction or underprediction of load. Secondly, we provide the first published investigation of baseline erosion from repeated dispatch of building load shifting. We observed that as the baseline period is pushed back further from the prediction day, the distribution of errors across baseline model predictions increases, with notable inflection points near the three-week erosion point for two of the three models. [ABSTRACT FROM AUTHOR]

• Building codes require about 20 powered safety devices in new U.S. homes. • Health and safety devices consume about 1% of U.S. residential electricity. • Safety devices in the U.S. are typically exempted from efficiency regulations. • Per-unit consumption is small, but each home will have many units. • Home medical devices are a growing use of energy. Miscellaneous electrical loads (MELs) are responsible for a significant fraction of energy consumption in buildings. This paper addresses one category of MELs: Safety, Security, and Health Devices (SSHDs). Common SSHDs include: electrical life safety equipment, smoke alarms, radon mitigation fans, and home oxygen concentrators. The installation or use of these devices is dictated by building codes, health providers, insurance companies, and other entities—none of which would ordinarily consider energy efficiency a priority. For this project, the most important residential devices were explored in terms of their governing regulations, functions, technologies, and energy use. The power consumption of 41 life safety SSHDs were measured. Individual power consumption of life safety devices is very small but many are typically required in every home. Devices performing the same functions have wide ranges in power use. Opportunities for reducing SSHD energy use were investigated, the results are summarized, and recommendations are made for further actions that could be taken to reduce SSHD energy use. SSHDs identified in this paper are responsible for at least one percent of current U.S. residential electricity consumption, but that number will climb steadily as existing buildings are upgraded and new types of SSHDs appear. [ABSTRACT FROM AUTHOR]

[Display omitted] The school building sector has a pivotal role to play in the transition to a low carbon UK economy. School buildings are responsible for 15% of the country's public sector carbon emissions, with space heating currently making up the largest proportion of energy use and associated costs in schools. Children spend a large part of their waking life in school buildings. There is substantial evidence that poor indoor air quality and thermal discomfort can have detrimental impacts on the performance, wellbeing and health of schoolchildren and school staff. Maintaining high indoor environmental quality whilst reducing energy demand and carbon emissions in schools is challenging due to the unique operational characteristics of school environments, e.g. high and intermittent occupancy densities or changes in occupancy patterns throughout the year. Furthermore, existing data show that 81% of the school building stock in England was constructed before 1976. Challenges facing the ageing school building stock may be exacerbated in the context of ongoing and future climate change. In recent decades, building stock modelling has been widely used to quantify and evaluate the current and future energy and indoor environmental quality performance of large numbers of buildings at the neighbourhood, city, regional or national level. Building stock models commonly use building archetypes, which aim to represent the diversity of building stocks through frequently occurring building typologies. The aim of this paper is to introduce the Data dRiven Engine for Archetype Models of Schools (DREAMS), a novel, data-driven, archetype-based school building stock modelling framework. DREAMS enables the detailed representation of the school building stock in England through the statistical analysis of two large scale and highly detailed databases provided by the UK Government: (i) the Property Data Survey Programme (PDSP) from the Department for Education (DfE), and (ii) Display Energy Certificates (DEC). In this paper, the development of 168 building archetypes representing 9,551 primary schools in England is presented. The energy consumption of the English primary school building stock was modelled for a typical year under the current climate using the widely tested and applied building performance software EnergyPlus. For the purposes of modelling validation, the DREAMS space heating demand predictions were compared against average measured energy consumption of the schools that were represented by each archetype. It was demonstrated that the simulated fossil-thermal energy consumption of a typical primary school in England was only 7% higher than measured energy consumption (139 kWh/m2/y simulated, compared to 130 kWh/m2/y measured). The building stock model performs better at predicting the energy performance of naturally ventilated buildings, which constitute 97% of the stock, than that of mechanically ventilated ones. The framework has also shown capabilities in predicting energy consumption on a more localised scale. The London primary school building stock was examined as a case study. School building stock modelling frameworks such as DREAMS can be powerful tools that aid decision-makers to quantify and evaluate the impact of a wide range of building stock-level policies, energy efficiency interventions and climate change scenarios on school energy and indoor environmental performance. [ABSTRACT FROM AUTHOR]

This paper introduces short-term load forecasting (STLF) using Generative Adversarial Networks (GAN). STLF was explored using several Artificial Intelligence based methods that offered excellent results. However, the usage of GAN models in this field is very limited, and usually works on creating synthetic load profiles to increase load datasets. This paper investigates the application of GAN for load forecasting by generating daily load profiles. Predicting the daily load is a challenging task that requires accurate and stable models that can capture seasonality and variation in load data. This paper presents a conditional GAN (cGAN) architecture, that uses only four exogenous variables (maximum and minimum temperature, day of the week and month), to predict a daily profile (24 h of the day). Several types of GAN have been compared such as Deep Convolutional GAN, Least Squares GAN and Wasserstein GAN. The generated load profiles were tested on one year of data and compared to the real load profiles. The proposed GAN models provided excellent predictions, averaging a Mean Absolute Percentage Error (MAPE) of 4.99%. [ABSTRACT FROM AUTHOR]

Short-term load forecasting for residential buildings is of great significance in ensuring the safe and economic operation of the power grid. However, most existing prediction methods focus solely on the temporal characteristics of individual buildings and ignore the spatial correlation between different buildings in a neighborhood. To tackle this problem and realize joint prediction for multiple buildings, a novel multi-task learning model with a selected-shared-private mechanism is proposed in this paper. Firstly, the temporal and spatial characteristics hidden in electricity consumption patterns are analyzed and represented by auto-correlation and cross-correlation, respectively. Then, a correlation-oriented combination strategy is proposed to build input feature set for the prediction model, and the temporal convolutional network is adopted to extract features. Furthermore, a novel selected-shared-private mechanism is designed for multi-task learning to improve the prediction accuracy, which can selectively utilize information from related tasks while learning private features. The proposed model is compared with other methods on the public dataset, and the results demonstrate that the proposed model can make satisfactory joint predictions for multiple buildings with more than 35% accuracy improvement over the support vector machine. • A correlation-oriented combination strategy is proposed to build the optimal input set. • A novel selected-shared-private mechanism is designed for multi-task learning. • The proposed model realizes superior joint load prediction for multiple residential buildings. [ABSTRACT FROM AUTHOR]

• Analysis of 15-minute sampled residential electricity consumption data of Dubai. • Households are classified using cooling consumption included in electricity bills. • K-Means clustering is used to group households based on their hourly consumption. • Consumption patterns helped to identify occupant behaviours in summer and winter. • Consumption patterns were cross-referenced to building type and occupant profiles. Analyzing residential load profiles and usage patterns is critical to making better decisions for demand-side management initiatives and designing strategies to get more people interested in energy savings. Therefore, analyzing load profiles, it is crucial to know how hourly consumption varies during summers, winters, weekdays, and weekends. In addition, determining the influence of occupancy, dwelling size, and building topologies on consumption is equally important to build predictive models. Therefore, this paper presents detailed research on the electricity consumption and profiles of the residential sector in Dubai based on the dwellings' characteristics and 15-minute resolution smart meter data. The data utilized includes the cooling systems, the number of occupants, and the physical characteristics of a dwelling, such as its typology, sizes, and number of bedrooms. First, using the K-Means clustering method, the authors grouped the households based on their consumption profiles. Second, the consumption patterns of each group of households were identified and organized based on similar consumption profiles over 24 h. Third, the authors applied several classification algorithms to assess the potential of using dwellings and occupants' characteristics to predict the patterns with which each household is associated. The analysis of consumption patterns showed that 43% of households with cooling included in their bills had the global peak demand at midnight during weekdays and weekends in summer. However, the global peak demand for cooling-excluded households occurs from 7:00 to 10:00 pm on weekdays and, in some specific cases, on the weekends, as early as 10:00 am. Finally, the methods used for classification were able to identify key characteristics driving the patterns of electricity demand and were well suited to this predictive modeling context. [ABSTRACT FROM AUTHOR]

The current energy crisis has drastically altered forecast electricity plans and budgets for European university campuses. This situation heightens the need to analyze their electrical consumption, with two main goals: identifying their patterns and promoting the development of renewable installations for these consumers. Previous research has focused only on aggregated demand data, with the studies being based on estimations and forecasts, and focused mainly on single buildings. Moreover, there is a lack of scientific papers that provide a replicable codebase for electricity analysis. Our work presents a novel methodology to overcome these research gaps, proposing the first comprehensive, replicable and scalable codebase to analyze electricity consumption in universities. It is based on three steps. The first comprises automated data collection of real electricity measurements at each electricity supply point. The second develops the complete analysis of electricity consumption. The last step parameterizes this consumption by identifying seasonal and daily profiles. The research was applied to the University of Castilla-La Mancha, campus Albacete (Spain) case study. The results revealed the 4 highest electricity-demanding buildings: Biomedical Complex, Higher Technical School of Industrial and Computer Engineering, Vice-rectorate and Library, and Higher Technical School of Agricultural and Forestry Engineering. The results are thus of great value for other educational buildings. [ABSTRACT FROM AUTHOR]

Optimal planning and operation studies of modern energy systems are tied up with medium to long-term predictions of energy demand. Deep learning algorithms have recently become significantly useful in this regard because of their capacity to learn load patterns with a high degree of fluctuation and uncertainty. Yet, empirical evaluation of different configurations of deep learning models, for enhancing prediction accuracy, has been mostly ignored in the preceding literature. In this paper, different architectures of deep recurrent neural networks (DRNNs) are explored and adjusted specifically for the purpose of performing medium- and long-term predictions of energy demands. We intend to create a bespoke DRNN for heating and electricity consumption prediction with a 1-h resolution. Moreover, hyperparameter optimization, which is a time-consuming and rigorous task in deep learning algorithms due to their abundance, dependence on the particular application, and empirical nature, is studied comprehensively. In this respect, the space of possible configuration variables is thoroughly explored to identify a collection of hyperparameters that enables the DRNN model to more accurately forecast energy consumption. We also benchmark the proposed algorithm's performance against other data-driven models, namely support vector machine (SVM) and gradient boosting (GB) regression. The proposed model outperforms SVM by 5.4% and GB regression by 7.0% in terms of energy forecasting accuracy. [ABSTRACT FROM AUTHOR]

Under the background of global warming, the world is experiencing many extreme climate events. The temperature fluctuation associated with climate change is often considered as a primary driving force of electricity consumption. To better explore the impact of temperature on electricity consumption, this paper aims to analyze the question from the perspective of income growth, i.e., the moderating effect of income growth on the response of urban residential electricity to temperature changes. Using the unbalanced panel data of 23 cities in the Yangtze River Delta Urban Agglomeration from 2004 to 2015, we construct the response model of residential electricity consumption to income growth, heating degree days, cooling degree days and their interactions. The results show that heating demand, cooling demand and income growth significantly increase residential energy consumption. However, urban residential disposable income growth weakens the positive effect of heating demand and cooling demand on residential electricity consumption. Because, urban residents have obvious demand for improving indoor heating comfort and cooling comfort, the moderating effect of income is shown as the crowding-out effect. Residents may adjust the structure of heating energy consumption (from electricity to natural gas) and upgrade their electrical equipment to high energy efficiency equipment, thereby reducing residential electricity consumption. This study also indicates that urban residential energy consumption is very sensitive to changes in permanent populations. The main contribution of this study is proposing the hypothesis of the negative moderating effect of income growth on temperature changes and expanding the response model of residential electricity consumption to temperature changes. Finally, this paper also proposes the adaptive strategies and policy implications for climate change. [ABSTRACT FROM AUTHOR]

• This paper determines the relationship between urban form and building energy use. • The surface-to-volume ratio and obstruction angle of buildings are important. • Consideration of the heating energy type and building use can yield reliable result. • The urban heat island effect exerts an impact on the electricity use in some months. • Correlation variation is caused by the building permit date. In the climate change era, urban geometry serves to ensure sustainability for urban planners since it notably affects building energy consumption. However, previous studies have mainly conducted experimental investigations under hypothetical conditions. Without empirical evidence, it is difficult to implement these findings under real conditions. This paper examined empirical data to determine the urban geometry effect on building energy consumption in a large, complicated urban area. Urban geometric parameters, including the surface-to-volume ratio, obstruction angle, and building orientation, were measured across Seoul, and their correlations with building energy use were analyzed. The results show that the surface-to-volume ratio and obstruction angle are important, despite the empirical data complexity. Additional data analysis indicates that the heating energy type and building use consideration yields more reliable correlation coefficient trends. The potential impacts of natural ventilation and the urban heat island (UHI) effect on building energy consumption were examined. The UHI effect, rather than shadows on buildings, predominantly impacts the building electricity use in August and September. The correlation variation caused by the building permit date was investigated. The results in this paper could help urban planners and designers develop blueprints to control urban geometry and establish relevant design guidelines. [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]

• GB domestic heat pump electricity demand in 2050 is modelled. • Modelling is based on monitored data from heat pumps and gas boilers in homes. • Uptake of heat pumps in all homes more than doubles peak GB electricity demand. • Moderate improvements in HP performance and dwelling fabric reduce peak GB demand. • Flexibility in DHW and space heating provision also reduce peak GB demand. To meet net zero carbon targets by 2050s, countries with cold winters, such as Great Britain (GB), will turn to low carbon domestic heating systems such as electric heat pumps. In this paper, a statistical model is developed using measured performance data from over 550 heat pumps installed in GB homes in 2012. The model is used to predict the additional load on the GB electricity generation and distribution infrastructure for various current and future (2050) climates, dwelling energy efficiencies and heat pump deployment scenarios. For a cold year in the 2020s, a 100% uptake of heat pumps in the existing GB dwelling stock gave a peak electricity demand for the heat pumps of 78 GW and an annual electricity demand of 189 TWh. When added to the other, existing electricity demand, this represents an increase in the GB peak electricity demand in excess of 100% and an annual electricity demand increase of around 60%. Making reasonable assumptions about heat pumps' future efficiency and use, and the future climate and dwelling stock fabric efficiency, and assuming 80% heat pump uptake and no load shifting, the peak heat pump electricity demand for a cold year in the 2050s halved to 37 GW. By shifting demand for water and space heating the 2020s cold year peak demand reduced by 15%. The work provides a more robust estimate of future GB heat pump electricity demands than any previously available. The predicted future ramp rates, peak and annual total electricity demands, are lower than predicted by previous analysts. [ABSTRACT FROM AUTHOR]

The building load forecasting with data-driven technologies guides the operation of the energy system. For new buildings or existing buildings with inadequate monitoring systems, transfer learning can be used to improve load forecasting accuracy. However, without an information integrity judgment, inappropriate migration features and incorrect transfer learning models will reduce the effectiveness of transfer learning. To bridge the gap, a data integrity judgment method is proposed in this paper to determine whether building features are absent. A max-relevance min-redundancy feature selection method with diffusion kernel density estimation (DKDE-mRMR) is established to select the key migrated features. With the transfer component analysis method used as the transfer learning model, the load data of three office buildings are taken as minimum feature set, source domain and target domain respectively for case study. The results prove that the CV-RMSE of load forecasting is over 41.0% by using insufficient information, whereas it can be reduced by 21.6% when information migration is deployed. Even if the target building after feature transfer is further missing the electricity consumption information for cooling load forecast, the CV-RMSE can still be maintained below 21.1% with good robustness. [ABSTRACT FROM AUTHOR]

• The proposed residential power scheduling strategy in smart grid is capable to achieve the optimal energy consumption profile. • The electricity payment and discomfort cost are coupled to constraint the DR to influence on users' consumption efficiency. • A novel DR algorithm based on cost-coupling constraint with distributed generation is developed to reduce consumers' expenditure while improving their comfort. • A day-ahead bidding price mechanism including real-time charging management is introduced to establish different household consumption patterns to guide the consumer to enjoy convenience while saving costs. • Various simulation experiments are performed to confirm that the renewable energy penetration is higher, consumers' consumption utility is higher, and the corresponding cost-efficiency is higher. Power scheduling is a major task for demand response (DR) to cope with the electricity consumption during the peak period in smart grid. In the residential sector, consumers trend to trigger their electrical appliances when the electric price is low. However, power scheduling including load curtailment, time delay and equipment power adjustment will change consumer's comfort level to produce discomfort cost. Thus, electricity payment and discomfort cost are coupled to constraint the DR. In this paper, we focus on renewable energy use by smart homes and consider cost-coupling constraints, taking an approach whereby we decouple the primary problem into several independent subproblems. A novel DR algorithm based on cost-coupling constraint with distributed generation is developed to reduce consumers' expenditure while improving their comfort. To that end, a day-ahead bidding price mechanism including real-time charging management is introduced, and a cost efficiency algorithm is designed based on electricity payments, discomfort and distributed generation. Simulation results demonstrate that the proposed method is effective and feasible when tested in real application scenarios. [ABSTRACT FROM AUTHOR]

• A comprehensive predictive method of electricity consumption and load for Chinese rural residences is proposed. • The hourly electricity usage behavior of different seasons are analyzed. • The predictive method is verified at individual and regional scale. • The influence laws of usage behavior on electricity consumption and load for a certain case is explored. Electricity consumption and load prediction, and the influence laws of usage behavior are essential for smart grid and policy guidance. This paper presents an electricity consumption and load prediction method based on a stochastic model considering the randomness and seasonality in the usage behavior of appliances, with the aim of exploring the influence laws of the usage behavior. According to the proposed method, a model was developed and verified by taking certain regional rural residences as examples, and the influence of model inputs on electricity consumption and load were explored. The results showed that the modeled total electricity consumption of individual and regional rural residences were consistent with the actual values. The prediction accuracy of the model could be improved by more than 30% compared with the deterministic model results. Furthermore, the model could reasonably capture the electricity load profiles on a daily as well a seasonal basis. Our research provides a comprehensive framework to predict the electricity consumption and load for Chinese rural residences from sampling and data acquisition to model development, and to preliminarily explore the power demand characteristics and significant influencing factors, which may provide valuable data for power grid design and photovoltaic power generation systems. [ABSTRACT FROM AUTHOR]

Smart control of domestic electric hot water cylinders could be used as a relatively inexpensive option to help bridge supply–demand mismatches in highly-renewable electricity grids in many countries. Evaluation of options for the smart control of these cylinders is made difficult by the lack of domestic hot water flow time-series data. In contrast, hot water cylinder electricity demand time-series data, either directly measured or imputed from whole house data, is often much more readily available. In this paper, we propose an inverse-problem approach that uses a physical model of a hot water cylinder to determine hot water flow information from electricity demand data. Once hot water flow information has been determined, it is then possible to simulate smart control of the hot water cylinder element such that it does not interfere with the provision of hot water. We illustrate this approach by using a simple physical model of a hot water cylinder to simulate the hot water flow of 19 houses with electric hot water cylinders from measured time-series electricity data. A smart control case study is then explored where these houses are part of a solar PV microgrid and the cylinder elements are controlled so as to reduce the battery storage needed by the microgrid to reach a certain level of self sufficiency. [ABSTRACT FROM AUTHOR]

Strict energy performance requirements have reduced the building's operational energy use and related environmental impact in the past decades. Consequently, the embodied impacts of buildings gained importance, and hence focussing on the life cycle impacts has become essential to further reduce the environmental impact of buildings. Life cycle assessment (LCA) is an internationally accepted method used for this purpose. Current LCA studies of buildings typically look at their whole life cycle but ignore potential changes in operational energy use due to climate change. However, the latter is expected to influence the operational energy use of buildings to a significant extent. This paper aims to improve the assessment of the operational phase by including variations in the yearly operational energy use caused by climate change and expected changes in the electricity mix as a strategy for climate change mitigation. This adapted dynamic assessment method is used to assess a Belgian office building. The results indicate that a Representative Concentration Pathway (RCP) 8.5 climate change scenario causes a decrease of 18 % for the end use of gas for heating and an increase of 32.4 % for the electricity use for cooling and ventilation. For the case study, the reduction of the environmental impact due to the decrease in gas use compensates the impact increase due to the higher electricity use. An increase in environmental impact in future would however occur if both heating and cooling are provided by electricity. Furthermore, depending on the changes that will occur in the electricity mix, the global warming potential of the office building will change by −13 % for an electricity mix dominated by renewables and by + 10 % for one with mainly production by gas compared to the situation with the current mix. However, the inclusion of a more extended list of environmental indicators shows that the overall environmental impact will only reduce by 4 % or even increase by 7 % compared to the situation with the current mix. [ABSTRACT FROM AUTHOR]

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

• Potential electricity demand charges reductions between 63% and 75% during a year. • Tested with real data from an industrial, an educational, and a residential consumer. • Methodology allows demand charges savings even without utility input or information. • Methodology based on use of arithmetic models and tree-based machine learning models. This paper presents a novel methodology that empowers virtually any electricity consumer paying for peak demand charges to proactively execute demand response actions even without receiving signals or information coming from the utility, and only when necessary to effectively reduce demand charges and user inconvenience. The proposed methodology employs different arithmetic models and tree-based machine learning models to determine an efficient electricity demand threshold value before the start of a billing period. This methodology is completely model agnostic so additional models can be integrated without changing the proposed process. The threshold value produced can be used to proactively trigger peak demand shaving and other demand response actions in order to reduce demand charges. The results obtained using real data showed that regression random decision forest based models outperformed different arithmetic models and other tree-based machine learning models at determining this threshold value for an industrial, an educational with solar photovoltaic electricity generation, and a residential consumer. The results also showed that the consumers evaluated could potentially achieve between 63% to 75% of total potential demand charge reductions during a year. These results translate to US$ 159.00, US$ 23,290.00, and US$ 107,389.00 in savings for the residential, industrial, and educational consumer respectively. [ABSTRACT FROM AUTHOR]

• At the mean temperature of 26.9 °C, 79% of the surveyed felt 'cool' or 'slightly cool' • At the mean operative temperature of 30.4 °C, 45.7% of the surveyed felt 'neutral' • Higher slopes of adaptive comfort equations than the standards was predicted. • The neutral temperature of 27.4 °C and 30.3 °C was predicted for AC and NV mode. • Minimum saving of 56% on cooling energy consumption could be achieved. Adaptive comfort considerations and passive design are crucial in tropical, hot and humid climates where the straightforward response to discomfort in this climate, such as in Ghana, has been the adoption of air conditioners and mechanical cooling. This approach, along with following the provisions of current international comfort standards, has resulted in higher electricity demand and excessive emissions of greenhouse gases into the atmosphere. This paper presents an adaptive thermal comfort field study in library buildings in the tropical Aw climate of Kumasi, Ghana considering naturally-ventilated (NV) and air-conditioned (AC) buildings. The proposed models in this study are compared with existing studies and current international standards. Using the Griffiths coefficient of 0.5, the mean neutral temperature of 27.4 °C and 30.3 °C were predicted for AC and NV mode, respectively. Although Fanger's predicted mean vote (PMV) method overestimates the extent of changes in thermal sensation votes (TSV) by indoor operative temperature in AC mode, the neutral temperature predicted from PMV (27.8 °C) is analogous to the one estimated using TSV. The adaptive equations for Kumasi's hot and humid climate predict higher slopes of 0.17 K−1 and 0.41 K−1 in AC and NV modes, respectively, than the standards; this indicates that the Ghanaian respondents were more sensitive to the outdoor temperature changes. The average difference of 2.1 °C in AC mode and 1.8–3.3 °C in NV mode were estimated when comparing the proposed model with those in the international standards [ABSTRACT FROM AUTHOR]

• A kNN-BS method is proposed to repair lighting and plug energy data. • Using cross-validation method to select the optimal k. • Using 2-step data classification method to decide day type. • Achieved higher accuracy than traditional k NN. The public building energy consumption monitoring platforms (BECMPs) play an important role in building energy conservation and energy-related decision-making. However, problems such as data missing and data mutation are quite common that they impede the effectiveness of the BECMPs. Based on the k NN algorithm, this paper proposes a k NN-BS data repair algorithm with the characteristics of electricity consumption trends taken into consideration. After data classification and cleaning, the data of hourly lighting and plug power consumption of an office building of Dalian University of Technology is repaired using the k NN-BS algorithm. The cross-validation method is used to determine the optimal k value. The relative repair error of the total daily electricity consumption of k NN-BS is less than 4% for working days and less than 6% for non-working days. Compared with k NN, the average CVRMSE of the k NN-BS algorithm is reduced by 1.36%∼1.59% for working days and 5.49%∼8.17% for non-working days. The k NN-BS algorithm shows excellent stability and robustness compared with k NN, polynomial and BPNN, which makes it very suitable for practical use, especially in efficiency-demanding scenarios. The k NN-BS algorithm can effectively improve the quality of public building energy consumption monitoring data and provide a theoretical basis and technical means for solving data quality problems in the BECMPs. [ABSTRACT FROM AUTHOR]

• An energy system integrates heating grid, EV smart charging, peak shaving, solar PV. • The demand ratio of DHW to SH defines the supply temperature of the heating network. • The electric peak demand can be reduced from over five time increase to 33%increase. • A model identifies the required degree of housing thermal efficiency improvement. • A modelling tool indicates energy system requirements and hourly electricity demand. Electrification in energy supply–demand plays a critical role in domestic heating and road transport, delivering an electrified community to reduce carbon emissions. This solution, however, places a significant power demand increase on the distribution networks. To ensure the security of electricity supply, an efficient energy system and energy demand reduction are essential. In this paper, a multi-vector community energy system, applying an electrified heating network, electric vehicle smart charging, community-scale peak shaving and photovoltaic (PV) generation, is demonstrated in three models to manage an electrified community. Firstly, a heating network model, comprising a central ground source heat pump, low-temperature district heating system, electric heaters and thermal storage, is established to measure the optimum distribution temperature. Next, an electrified community model illustrates hourly electricity demands and performances of a community energy system, which is then used to identify the required degree of housing thermal efficiency improvement (i.e., heating demand reduction). The third model evaluates decentralised PV/storage units to maintain the power demand below a targeted power. Modelling results show that the demand ratio of domestic hot water to space heating determines the distribution temperature, which indicates the temperature is increasing with growing housing thermal efficiency. Moreover, the electrification of a community could increase the peak power demand on the highest demand day by over five times, converting heating demands into electricity directly. This significant peak demand can be possibly reduced to only a 33% increase by employing a community energy system. The model of PV/storage units is validated through a 12-week assessment. Ultimately, a modelling tool is developed by assembling the mentioned models, providing four pathways to attain electrification. Users can adjust specific parameters and database to align with the local conditions. The results indicate the requirements of building a community energy system and electricity demands in the highest consumption period. [ABSTRACT FROM AUTHOR]

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

Today, Building Energy Models (BEM) have become essential in regulatory compliance calculations, the correct assessment of it's Air Conditioning (AC) systems is critical for the reduction of the performance gap between BEMs and reality and increase the accuracy of evaluating buildings energy performance and it's systems efficiency. Given that multi-split Variable Refrigerant Flow (VRF) systems have grown in the market in recent years becoming a particular trending solution to achieve building indoor comfort; the present paper focus on technical issues when modelling such VRF systems inside EnergyPlus, a white-box simulation environment, especially regarding the effects weather conditions have on the behaviour of VRF systems and it's correlation with the AC system performance curves. The study performs an empirical validation of an optimization-based calibration methodology assessing multiple levels: average interior temperature of the different building spaces and electric energy consumption from VRF outdoor unit. It is performed using fifteen minute time-step seasonal data obtained from a fully operational building located in a typical Mediterranean climate (Greece), adjusting the parameter and curve values of the VRF system using a genetic NGSA-II algorithm (Jeplus software) for both summer and winter conditions. The generated BEM captures the building's hourly performance for summer conditions using 1717 hours to fit into international standards. Complying with the requirements of the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) Guidelines 14-2002 for hourly energy consumption, reaching an NMBE ⩽ ±10% ,Cv(RMSE) ⩽ 30% and R2 ⩾ 75% while keeping indoor temperatures on every room with a RMSE ⩽ 1 ° C. The resulting BEM proved stable during the 2077 hours of it's summer evaluation period, fitting into the new unseen weather and building operation conditions of 2020 which can be considered a step forward in the area of calibrating white box models. While for winter conditions the study demonstrates the value of the calibration methodology while presenting the importance of weather influence on VRF systems. Using a total of 802 hours the applied technology greatly improves the results from the baseline model, reaching a partially calibrated BEM model for winter. Which reinforces the fact that regardless of how good a baseline model is, building operating conditions and weather may will always generate a design/performance gap and therefore the calibration of a BEM is unavoidable. [ABSTRACT FROM AUTHOR]

[Display omitted] • Multimodal occupancy detection framework for residential buildings. • Two variants of Occ-STPN models with feature level fusion and decision level fusion. • Feature extraction and occupancy detection models for various data modalities. • Transferability of a trained occupancy detection model from one building to another. • Occupancy prediction performance evaluation on three open-sourced datasets. For several years now, smart building energy systems have been a research area of intensive activity. In light of the increasing need for sustainable buildings and energy systems, this trend motivates an increasing need for a solution to reduce carbon dioxide emissions and improve energy efficiency. This paper proposes a high-performing and transferable occupancy detection framework that combines sensor data from different data modalities, including time series environmental data (temperature, humidity, and illuminance), image data, and acoustic energy data using ensemble method. To draw out the best prediction performance in each modality, the proposed framework was developed, including various models that were designed to learn the occupancy patterns reflected in the physical data streams. To tackle the time series environmental data, we designed two variants of an occupancy detection spatiotemporal pattern network (Occ-STPN) that performs both feature level and decision level fusion, respectively. We also propose a new metric; the fading memory mean square error (FMMSE), that provides a fair evaluation and penalization of delayed occupancy predictions. Multiple open-sourced datasets, including the Electricity Consumption and Occupancy and the University of California, Irvine's (UCI) building occupancy detection dataset, along with our own real data collected from six different houses, were used to validate the algorithms' performance. The experimental results presented herein break down the performance for each sensing modality, and a detailed analysis of the performance is also discussed. [ABSTRACT FROM AUTHOR]

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

• A net zero energy wooden structure building in severe cold zone was designed in severe cold zone. • A new type of water-passing wall was designed and applied in the wooden structure building. • Renewable energy systems are adopted to pursue net zero energy consumption for wooden structured buildings in severe cold areas. • Study on the operational performance of wooden structure building. • Evaluated the energy criteria and economy of the wooden structure building. At present, there is no lack of research on net zero energy buildings (nZEB) in China, and most of the research on "nZEB" focuses on buildings using traditional building materials like rebar concrete, brick, etc. In this paper, we take a wooden structure residential building in the severe cold zone as the research object, design a new type of water-passing wooden structure wall, and explore how to achieve net zero energy consumption through appropriate methods in the design stage. During the operation of the wooden structure building throughout the year, the solar photovoltaic (PV) system provides electricity to the building. In the heating season, we will supply groundwater or shallow geothermal water into the water pipes in the wall, and the solar thermal system provides hot water for heating. According to the whole year operation of the wooden structure building, the indoor environment meets the requirements of comfort standards, and the designed envelope structure of the wooden structure building has an excellent thermal performance. The energy consumption was reduced by 62.9% compared with energy-saving buildings without water-passing walls. The electricity generation capacity of the solar PV system is more than the actual electricity consumption of the building, the energy criteria are met the relevant requirements of the net zero energy building, and its economy is also evaluated. [ABSTRACT FROM AUTHOR]

• End-use of electricity consumption in residential buildings in Gulf Cooperation Council countries was presented. • Discrepancies in certain indicators have been observed by the end-use of electricity consumption. • Improving the efficiency of air conditioners and thermal insulation of the walls are the best measures to conserve electricity in residential buildings. • The results can be used in areas with a similar climate, living standard, buildings types, and the inhabitants' behavior. Energy conservation and improvement in energy efficiency yield multiple technical, economic, environmental, and social benefits and realize comfort and sustainability of residential buildings (RBs). This review paper presents the end-use of electricity consumption in RBs and the results of economic analysis. In addition, a description and discussion of studies on energy conservation measures, Net Zero Energy Building, and renewable energy in RBs are discussed. The percentage of end-used energy consumption for different purposes obtained in many studies are often not consistent. Moreover, discrepancies in certain indicators have been observed, necessitating the need to clarify the factors in the consumption of electricity in RBs. Further, a few studies used the term energy consumption in buildings (total or electrical); however, specifying the type of buildings to identify and use the necessary indicators of energy consumption in each type of building are desirable. Furthermore, most studies emphasized improving the efficiency of air conditioners and thermal insulation of the walls as these are the best measures to conserve electricity. [ABSTRACT FROM AUTHOR]

• Estimation of annual electricity consumption (EC) of more than 1,700,000 buildings. • EC models based on top-down, bottom-up, regression analysis, GRU and GIS mapping. • Contribution of building and socio-demographic variables on EC. • Effect of temperature on actual and predicted EC used for heating and cooling. • EC is spatialized on different territorial scales. This study financed by ERDF and the Wallonia Region estimates the annual electricity consumption (EC) of building stock, including the 3 building sectors namely residential, tertiary and industrial building. The estimation takes into account appliances, electrical heating, cooling, lighting, cooking and EC by m2 on a building level. The results are spatialized on different territorial scales. Using cadastral data of more than 1,700,000 Walloon buildings and annual EC data from a sample collected in 2012 from the energy reports, the paper assesses the EC of the whole building stock and tests to what extent different types of variables (building factors and socio-demographics) explain annual EC. It then shows which individual variables have the highest explanatory power. In contrast to many other studies, the research recognizes the problem of multicollinearity between predictors in regression analysis and uses Lasso regression to address this issue. Three separate regression models were used to study the predictors of annual EC of residential, tertiary and industrial buildings. EC building factors (appliances, auxiliary and main heating, domestic hot water and cooking) explained the largest share which is 66.46% of the variability in EC for residential buildings whereas the EC usages share for tertiary buildings (lighting, heating and domestic hot water, air conditioning, cooling, etc.) is about 50.53% and 38.55% for industrial buildings. Socio-demographic variables on their own explained about 61.59%, 26.34% and 3.41% of the annual EC, respectively for residential, tertiary and industrial buildings. Hence, the building variables present the highest explanatory power for EC, presumably because heating and cooling EC are included in this study. The study highlights that when attempting to explain EC related to Walloon households, including heating and cooling EC, appliances usage has the strongest predictive power in residential buildings. On the other hand, the projected decrease in EC use for heating in existing residential buildings is −8.82% and −10% for existing tertiary buildings while the projected increase in EC use for cooling in existing tertiary buildings is + 11.94% from 2012 to 2050 on a regional scale. These trends follow the predicted regional heating degree-days (HDD) of −11.76% and cooling degree-days (CDD) of 14.04% for the same period based on the gated recurrent unit (GRU) an implemented deep learning (DL) model. In addition, the produced EC maps on different territorial scales show that the highest EC is seen in large and main cities in general. [ABSTRACT FROM AUTHOR]

• A novel attention mechanism based energy demand forecasting algorithm. • Textual data mined to improve the forecasting capability. • Validated on a real-world electricity consumption dataset. Forecasting energy demand of residential buildings plays an important role in the operation of smart cities, as it forms the basis for decision-making in the planning and operation of urban energy systems. Deep learning algorithms are commonly used to reliably predict potential energy usage since they can overcome the issue of dependency on long-distance data in energy forecasting relative to the standard regression model. However, there are still two problems to be solved for energy forecasting, including the encoding of categorical characteristics and adaptive extraction of the most relevant characteristics for the use in predictions. To address the problems, we proposed a sequential forecasting model for medium- and long-term energy demand forecasting based on an embedding mechanism and a two-stage attention-based long-term memory neural network. An empirical study was conducted on three years of daily electricity consumption data from the residential buildings of the Pudong district of Shanghai to evaluate the model. The results show that the model can effectively extract the key features that are highly correlated with energy consumption dynamics by employing long-term dependencies in time series. In addition, the hybrid model outperforms others in terms of long-term forecasting capability. This paper also discusses future research directions and the possibilities for applying deep learning techniques in the energy sector. [ABSTRACT FROM AUTHOR]

• Packaged ice storage is evaluated for central and distributed HVAC systems. • An OpenStudio measure to model packaged thermal energy storage (TES) is developed. • Peak electricity demand during summer months is reduced by 32.2–36.6%. • Annual fan energy use increases by 7.3–10.7% with the addition of packaged TES. • Potential cost savings are assessed for multiple time-of-use utility rates. As renewable portfolio standards enforce the expansion of renewables on the U.S. grid in the coming years, old storage technologies must be re-evaluated for a dynamic, interactive future grid. Ice thermal storage, traditionally for diurnal load shifting on large central chiller plants, is also viable in packaged devices for most buildings that lack central plants. However, modeling of these unitary thermal storage systems (UTSS) has been very limited to-date. To help make packaged thermal storage modeling more accessible, this paper presents a recently developed OpenStudio measure for rapid analysis of UTSS. Using this measure, we assess the UTSS implementation in new and retrofit retail buildings with both packaged single zone air conditioners (PSZAC) and packaged variable air volume (PVAV) systems. Each implementation is evaluated on annual energy use, fan energy use, load shifting efficiency, daily unused ice availability, and potential cost savings under various time-of-use rates. Deficiencies with schedule-based control are highlighted by comparing the daily unused ice and the cooling-electricity load duration curve. Annual energy use increases by 1.2–3.7%, but on-peak electric demand is reduced by 32.2–36.6%. [ABSTRACT FROM AUTHOR]

• Two-year monitoring campaign of IEQ parameters of a nearly zero energy building was firstly presented in China. • The nearly zero energy building can provide the indoor with a comfortable thermal environment and good air quality with less energy demand. • The overheating problem in the whole year would be especially paid attention during design and operation. • The occupant behavior has a significant impact on energy consumption and thermal comfort. The paper presents a two-year field measurement and assessment of a nearly zero-energy Building in cold zones of China. These parameters of indoor environment quality (IEQ) and electricity consumption for HVAC are recorded. According to the measurement data, the most of indoor temperature was controlled in the range of 23 °C ~ 26 °C and relative humidity (RH) mainly fluctuates within the range of 30% ~ 60%. The concentrations of carbon dioxide (CO 2) and particulate matter (PM 2.5) are lower than the relevant standard requirements in most of the time. The average electricity consumption for HVAC was 33.05kWh/(m2·a) and the heating and cooling demands could be reduced by 55.2% and 54.0%, respectively, compared with existing buildings. In general, the NZEB can provide indoor comfortable thermal environment and indoor air quality with less energy demand, but the overheating problem in summer and effect of occupant behavior should be especially paid attention during operation. Overall, the experience from the actual operation of NZEBs will help China to reach its goals of reducing carbon emissions and addressing climate change through realizing the energy-saving potential of NZEBs. [ABSTRACT FROM AUTHOR]

[Display omitted] • The profile DSR estimation method features a better balance of error compared with previous methods. • Clustering of detailed usage data to create profiles is a viable approach for improving DSR estimation. • Profiles can provide greater account of estimation uncertainty compared to existing deterministic methods. In principle, Demand Side Response (DSR) is increasingly seen as a critical component of a low-carbon electricity network with renewables as main sources of generation. In practice, DSR has been slow to emerge in most electricity markets of developed and developing countries. One of the main reasons for the slow penetration of DSR is the difficulty to assess the flexibility potential of individual sites. This paper develops a new DSR estimation method which uses detailed profiles information based on data on Heating, Ventilation and Air Conditioning (HVAC) chillers in five UK hotels between 2013 and 2017 and applies a combination of clustering analysis and subsequent stochastic sampling using cluster-weighted date-based predictors. Findings show that the profile DSR estimation method features a better balance of error compared with previous methods. [ABSTRACT FROM AUTHOR]

[Display omitted] • A repository of 171 UHI papers were systematically reviewed to address relevant questions. • An endogenous research geospatial distribution, disadvantaging UHI most vulnerable regions was observed. • UHI strategies, tools, techniques, indexes and data source trends were identified through through data curation. • Seven major urban fields of impact were identified and multidimensionally analyzed. • Future research opportunities on UHI regulatory framework development were proposed. Scientific research on Urban heat islands (UHI) and urban. overheating has set new challenges for societies regarding risk and vulnerabilities, energy demands and mitigation strategies to favor urban environmental quality conditions against urban pollution. This review manuscript addresses systematically 171 relevant studies to identify and characterize gaps in the field of knowledge, through a robust evidence-based analysis focused on identifying taxonomic recurrences, trends in the study of urban fields of impact, global leaderships, mitigation strategies, methodological divergences, remote sensing indexes, data sources, legal frame, among other significant aspects on the context of environmental degradation and energy. The results showed that studies were carried out in 37 countries and 92 cities, mainly in North America (33%), Europe (31%), and Asia (27%) although mainly concentrated in five countries (59%) with an endogenous bias towards domestic microscale purposes. Prevailing topics, multidisciplinary interactions and regulations are focused on the study of Urban Environmental Quality degradation (95%) and on issues related to Energy (25%) demand and efficiency with applications on land cover with high interest on material science, and urban design and planning. The need to implement multiscale studies, motivate policies oriented to prevent health vulnerability and promote energy safety for urban resilience is imperative. [ABSTRACT FROM AUTHOR]