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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]

This paper examines the extent to which different household socio-demographic and building characteristics contribute to high energy use in Social Housing. Using data from the sample created in the framework of the EU funded ICE-WISH project (social housing pilots in ten European countries, of around 25 dwellings each), an odds ratio analysis was performed to examine the effect of socio-demographic and building's structural determinants on high residential energy consumption. The analysis is performed separately for electricity use for lighting and appliances and for energy consumption for space and water heating. Results indicate that high electricity consumption in social housing is related to both socio-demographic characteristics of the occupants and characteristics of the buildings in which they live (OR = 5.6 for households with three occupants compared to single households and OR = 3 for households living in houses greater than 80 m 2 ), whereas high energy consumption for space and water heating is only related to building characteristics (OR = 1.15 for households with three occupants compared to single households and OR = 4.77 for households living in houses greater than 80 m 2 ). The outcomes of this paper call for future energy policy to intelligently incorporate traditional and behavior-based energy efficiency interventions for reducing residential energy consumption and CO2 emissions. [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]

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]

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]

• Operating time of SMEs is estimated using a SOM and hourly demand data. • The estimated operating time is used for hourly AC demand disaggregation. • The average errors of yearly AC demand estimation are better than 10%. • Hourly disaggregation highlights season and time period of AC demand peaks. Despite the growing penetration of smart meters and energy management systems in recent years, few tools have been proposed for energy demand data analysis and visualization to promote energy conservation in small- and medium-sized enterprises (SMEs). In this paper, we propose a new method consisting of operating time estimation and simplified disaggregation based only on the SME's hourly electricity demand data and actual outdoor temperature data. The method first estimates the operating time using a self-organizing map, which categorizes the hourly data as operating, half-operating, or non-operating time and eliminates manual parameter adjustment that requires user trial and error. Then, using the estimated operating time, our simplified disaggregation method estimates hourly air conditioning (AC) demand considering the variation in each office's temperature at which air conditioners are switched on. The method was evaluated using a public business energy management system dataset from Japan, and the average errors of yearly AC demand estimation were better than 10%. The outputs of the proposed method may provide information that is more relevant, reliable, and specific to each customer and efficiently promote energy conservation measures in SMEs. [ABSTRACT FROM AUTHOR]

Urban expansion, driven by population and economic growth, has been a major contributor to increased levels of energy consumption across the globe. Beirut, Lebanon's capital, is no exception in facing a surge in its demand for electricity as it expands. However, with frequent power outages, underpinning a largely problematic power sector, Beirut's demand for electricity is becoming a real hurdle that impedes the city's economic growth and development. This paper introduces a near-city-scale building energy model, BEirut Energy Model BEEM, which estimates the building stock's electricity consumption in two different districts in Beirut. The methodology uses rule-based expert data for an archetypal classification of the buildings based on their functions and periods of construction with their corresponding attributes including the number of floors, number of apartments, and bimonthly electricity consumption to generate a 3D model for 3630 buildings coupled to the hourly weather conditions and topographic map, which is then simulated in EnergyPlus. The predicted consumption of 2311 buildings is then calibrated with actual available metered data, to adapt the model to Beirut's occupancy and users' behaviors. Calibrated results are mapped to reveal the spatiotemporal distribution of energy peak demands which provide insights for future interventions. An analysis of the spatial distribution of electricity use demonstrates a spatial clustering that underlies urban energy demand which can be used for smart grid zoning. [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]

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]

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]

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]

• 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]

[Display omitted] • Discussed the current status of cooing technologies in data centers. • Introduced and reviewed existing phase change cooing in data centers. • Compared and summarized the characteristics of phase change cooling systems. • Analyzed the application limitations of phase change cooling technologies. • Explored the future research direction of phase change cooling technologies. With the advent of big data era, data centers (DCs) related energy use accounts for approximately 3% of the global electric power consumption. As the augmentation of data-processing performance and thermal density in DCs, its energy use will undoubtedly continuously burst. Meanwhile, the potential safety hazards, caused by IT equipment local overheating, threaten the safe operation and restrict the further development of DCs. Thus, efficient cooling approaches should be applied in DCs to ensure its safe operation and optimize its thermal environment and cooling efficiency. Phase change cooling (PCC) technology is regarded as one of the effective and widely-used cooling methods, which have been applied in DCs for several years. In this paper, the up-to-date PCC technologies are reviewed and summarized, as well as the latest progress in DC cooling field. Four main PCC technologies are discussed in this paper, namely independent heat pipe cooling, integrated heat pipe cooling, two-phase immersion cooling, and cold storage systems. Finally, the shortcomings of the current researches on the PCC methods are summarized, while some suggestions for future researches are provided to promote the application of PCC technologies and achieve safe operations and energy savings in data centers. [ABSTRACT FROM AUTHOR]

Addressing the large energy consumption of hotels requires an understanding of the factors that drive this consumption. This enquiry is crucial for South Africa which has experienced significant strain in meeting its domestic energy demand. This has occurred alongside increases in international tourists, adding to the pressure on already strained resources. This paper tests hypotheses on drivers of energy consumption in hotels using a novel panel dataset which presents daily consumption data for 22 hotels across South Africa. Findings from various specifications of the Dynamic Random Effects Model suggest that the number of rooms in a hotel, the services and facilities offered, and climatic conditions are strong drivers of consumption. While the role of occupancy could not be robustly ascertained due to severe data limitations, findings indicate that price regulation plays a significant role in curtailing electricity consumption, even in high-end hotels. Results further suggest that in the design of guidelines for energy efficiency in South African hotels, the energy consumption of the facilities and services offered should be the first point of call, and the strenuous impact of extreme weather conditions on energy consumption needs to be factored in at the phase of building design and construction. [ABSTRACT FROM AUTHOR]

The anthropogenic heat, which primarily consists of emissions from human metabolism, and also the rejected or waste heat from electricity consumption in buildings due to human activities and the requirement for maintaining the thermal environment by human being, is considered as an important contributor to the urban heat island phenomenon and has been studied worldwide. High energy consumption in the urban buildings impacts the urban surface energy budget and causes the emission of anthropogenic heat flux into the atmosphere. Today, with the growing population and growing activities in buildings, much of the concern is focused towards the decrease of anthropogenic heat through low energy consuming and more energy efficient buildings. This study, utilized a validated occupancy probabilistic input model to estimate different building retrofitting solutions on the impact of building energy consumption and further as the basis for building cooling associated anthropogenic heat study. A holistic evaluation of passive, active and combined strategies is tailored for application with high occupancy variation situation such as campus buildings. With the best retrofitting solutions, the campus anthropogenic heat is reduced from 150 W/m 2 to 128 W/m 2 , which is considered as very effective. Although the campus buildings are used as an example, this paper demonstrates a generalisation method for the application of retrofitting strategies and efficient building systems in estimating the reduction of anthropogenic heat. The results demonstrate high potential for the campus building retrofitting and the values can contribute as a database for future urban heat island effect estimation. [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]

This paper reviews and analyzes technological design options, which have become available to date for BIPV systems on roofs and facades, independently of specific products or building projects. This means that this survey does not analyze existing products or realized buildings, but provides an overview of the technologies for BIPV. The starting point is an analysis of the relevance of BIPV technologies for the decarbonization of energy systems, providing energy for direct use of electricity and sector coupling together with an analysis of the German BIPV market. The paper presents the wide range of technical design options for BIPV systems and categorizes and analyzes them to provide a structured overview. This comprises a detailed analysis of the design options for BIPV modules, in which not only the design options for the PV cell layer were comprehensively investigated, but also the different variants of embedding materials, front and rear cover materials, additional interlayers and electrical module layout. Two fundamental module-level design options were investigated in particular detail: The use of PV cells as basic elements of patterns and the use of color to conceal the PV cells. Subsequently, options for the design of complete electrical systems are reviewed, ranging from sub-module level design parameters to building energy systems. Design options for the constructional integration of BIPV modules in the building envelope complete the review of technological design possibilities. [ABSTRACT FROM AUTHOR]

Currently, intermittent-operating escalators are gaining popularity on the market. The energy consumption of such devices highly relates to the passenger traffic profile during a day. This paper models the energy consumption of intermittent-operating escalators with four different passenger profiles and analyzes the resulting differences in the daily electricity consumption. The passenger profiles and their impact on escalator energy consumption are based on multiple long-term measurements and previous research studies. The results indicate that passenger volumes and density during a day affect the overall energy consumption drastically. The main reason for the differences is the principle of intermittent-operating escalators, the “power demand cycle”, along with the Variable Speed Drive. [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]

• 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]

The topic of energy efficiency applied to buildings represents one of the key aspects in today's international energy policies. Emissions reduction and the achievement of the targets set by the Kyoto Protocol are becoming a fundamental concern in the work of engineers and technicians operating in the energy management field. Optimal energy management practices need to deal with uncertainties in generation and demand, hence the development of reliable forecasting methods is an important priority area of research in electric energy systems. This paper presents a load forecasting model and the way it was applied to a real case study, to forecast the electrical consumption of the Cellini medical clinic of Turin. The model can be easily integrated into a Building Management System or into a real time monitoring system. The load forecasting is performed through the implementation of an artificial neural network (ANN). The proposed multi-layer perceptron ANN, based on a back propagation training algorithm, is able to take as inputs: loads, data concerning the type of day (e.g. weekday/holiday), time of the day and weather data. In particular, this work focuses on providing a detailed analysis and an innovative formal procedure for the selection of all the ANN parameters. [ABSTRACT FROM AUTHOR]

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

• A 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]

• 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]

• 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 communal areas can have different levels of energy performance in the 15 types of public residential buildings under study. • Spatial design features can evidently affect the energy consumption in the communal areas. • The ability to harvest daylight serves as a key determinant of energy efficiency. • Both econometric and machine learning techniques are proved to be effective to generate reliable findings. In this paper, we construct a unique database for 1228 residential buildings in Hong Kong to investigate how the spatial features of these residential buildings affect the electricity consumption in the communal area. We choose Hong Kong for this analysis as the city owns a large number of standard-type residential buildings managed by the public institution, which could be affected strongly by environmental policies. Both the machine learning method, based on the Least Absolute Shrinkage and Selection Operator (LASSO), and econometric regressions are adopted to analyse the data. We first utilize the machine learning LASSO technique to identify the most relevant factors for the subsequent econometric analysis. Our results show that the electricity demand for relatively low consumption building types, such as Twin Tower, is 6% lower than that of the high consumption building types. Newly constructed buildings usually belong to the medium consumption types, with the estimated monthly electricity consumption per apartment in communal areas to be around 50.2 kWh on average in 2020. These findings shed light on the nexus between spatial features and energy use for complex buildings, potentially contributing to the better crafting of energy-saving policy and the improvement of residential building programmes. [ABSTRACT FROM AUTHOR]

• The impacts of COVID lockdown on six aged care communities' energy and peak demand are studied. • The lockdown period was compared with the same period of similar temperature in previous years. • The impacts of COVID lockdown varied enormously in different climate regions. • Effectiveness and limitations of existing energy KPIs are discussed. In late March to April 2020 residential aged care facilities (RAC) in Australia were under COVID-19 lockdown. This paper explores whether the resultant restrictions on entry and exit and elimination of site group activities within RACs impacted on the total electricity use, peak demand and electrical load profiles. Six RACs in four different climate zones are analysed, comparing historical electricity load and demand profiles with the lockdown period. The facilities in warm regions showed largest reductions in energy use and peak demands. There was a peak demand timing shift in temperate regions and hot regions' changes were negligible for energy use or peak demands. This study revealed the limitations of using aggregate data as the key performance indicator (KPI) – energy use per bed. Also, KPIs in relation to cooling degree days (or total cooling and heating degree days) have been examined and are not recommended for temperate regions or temperate seasons. The potential CO 2 emission reduction from onsite renewable generation is quantified. Further research is to investigate energy use changes at circuit levels under lockdowns, and develop more nuanced KPIs that include the rate of energy use and the timing of that use. [ABSTRACT FROM AUTHOR]

• Environmental and economic performances of multi energy system(MES) was evaluated. • The operation models of MES cause the difference of environmental performance. • System environmental performance also affected by the CO 2 emission coefficient. • The price of equipment and energy mainly affect the economic performance of MES. Due to the improvement of people's living standards, uptake of multi-functional household appliances, electricity consumption accounts for a rising ratio in the proportion of primary energy usage. Currently, electricity accounts for more than 50% of primary energy usage and city gas is about 21% of total. The aim of this research focus on improving the efficiency of household energy usage and optimizing the economy of residential energy systems. This paper classified the detail electricity consumption and generation for a Japan typical household with Hybrid Fuel cell and Photovoltaic System (HFPS) based on the history monitored data. Then a simulation model of the Hybrid Heat pump and Photovoltaic System (HHPS) was designed under the same user load for analyzing the energy consumption. Finally, the economy of the two systems is compared and optimized by considering the carbon tax, equipment price and energy price fluctuation. In general, by comparing the environmental and economic performances of both two systems, the results showed that HFPS has a better environmental performance than HHPS, but HHPS presented an economic advantage over HFPS. In addition, with the change of equipment price and the development trend of energy price in the future, the economic performance of HFPS is gradually improved, which is better than HHPS and has more development prospects. The results of this work provide policy guidance for the future Japanese government's promotion of residential fuel cell systems, while choosing the optimal path between user energy characteristics and power contract scheme. [ABSTRACT FROM AUTHOR]

Control sequences for air distribution and terminal systems in heating, ventilating, and air-conditioning (HVAC) aim to achieve a balance in the system outputs, i.e., maintain thermal comfort and indoor air quality (IAQ) with minimal energy use. ASHRAE Guideline 36 (G36) – High-Performance Sequences of Operation for HVAC Systems , is the result of ASHRAE research project 1455-RP intended to develop standardized sequences of operation to achieve more effective use of existing controls. This paper complements G36 by evaluating the influence of the uncertainty inherent in the control components (e.g. sensors and actuators) on the system outputs of a multiple zone variable air volume (VAV) system. The system outputs under study were zone air temperature, relative humidity, carbon dioxide (CO 2) concentration, and site electricity use. To evaluate the effects of uncertainty in HVAC systems with advanced sequences of operation, this work applies a Monte Carlo uncertainty analysis to a detailed Modelica building energy model that has been programmed with G36 control sequences. Uncertainty models were integrated with the deterministic models of the building and the control sequence at small time scales to represent frequencies in which a real-world building automation system (BAS) samples its signals. The impact of uncertainty was quantified using annual simulations. Specification of the accuracy levels in the components of the control system were evaluated by the means of: 1) uncertainty analysis for low, medium, and high severities of accuracy in the components to identify relation between performance requirements and component accuracy, and 2) sensitivity analysis to identify the sensors and actuators where the impact of uncertainty on the system outputs is most influential. [ABSTRACT FROM AUTHOR]

• An IoT-based monitoring system platform for a solar water heating system. • A comprehensive audit procedure for a solar water heating system through IoT-data. • A number of control strategies for energy efficiency and savings improvement. • Verification of control strategies via experiment on a solar water heating system. This paper studies a mechanism to investigate and to improve the energy efficiency of a solar water heating system (SWH) in a hospital building located in Singapore. We utilize the data collected from Internet-of-Things (IoT) to gain information on different environmental and operational aspects of the considered system, that determine opportunities for improving the efficiency of various sub-systems of the SWH, and develop control strategies for efficient operation of the overall system. Through the data collected by IoT, such as water flow, heat pump and water pump operational schedule, availability of solar, and electricity consumption, we perform a comprehensive energy audit to analyze the efficiency of sub-systems and the performance of overall system as well. Based on the insights obtained from data analysis, corresponding control strategies for various sub-systems are developed to improve the efficiency of the overall system and reduce the operational cost of the SWH. Finally, the effectiveness of the proposed control strategies is confirmed through experiment on the site of the considered SWH. [ABSTRACT FROM AUTHOR]

• The energy consumption of 38 Chinese district heating systems was investigated and compared. • Method to break down the consumption and evaluate the operational problems was presented. • Field measurements were conducted to evaluate problems leading to high energy consumption. • Overheating was responsible for 23% of total heat consumption. • Resistance, operation flow, and pump efficiency were three major problems in distribution networks. Worldwide, China has the largest district heating (DH) area and is also the fastest growing country. This paper presents field measurement results for operational energy consumption from 38 DHSs to fill in the gaps in understanding of the current operational issues of the district heating system (DHS). The average heat consumption during the heating period was 98.6 kWh/m2 and the monthly average electricity consumption for circulating pumps in the primary and secondary networks was 0.27 kWh/(m2 × month) and 0.29 kWh/(m2 × month), respectively. Thereafter, a systematic method for breaking down the energy consumption and quantitatively evaluating the underlying problems is presented. Based on several detailed measurements conducted in typical heat-exchange substations (HESs), it was found that overheating was the most prominent problem in energy-efficient buildings, constituting 23% of the total heat consumption. Another problem encountered was heat loss through pipes, which constituted approximately 5%–10% of the total heat consumption and was highly dependent on the actual conditions. In distribution networks, problems such as high resistance, high operational flow, and low pump efficiency were also identified and analyzed. Finally, practical energy-efficiency suggestions and future potential targets for the Chinese DHS are discussed from both a design and operational perspective. [ABSTRACT FROM AUTHOR]

• This study describes the current state of energy use from field survey on household. • The average electricity use of Nepal was 2.06 GJ/household/year, which is quite smaller than that in developed and even other developing countries. • The monthly rate of firewood use was found to be higher than other developing countries. • The amount of electricity use was related with household income, occupation and family size. • There is a significant relationship between electricity use and income regarding to the education level of family heads. Energy use is required for securing a certain living standard. Availability of energy sources, electricity in particular, is the basic requirement for the quality of living. The rationality of household energy use is not only for improving individual living conditions, but also for the economic growth of a nation. To formulate the national energy strategy plan, it is important to know the current energy use and availability conditions. This study describes the current state of household energy use available from related papers and also from a field survey on household energy use including electricity use. We have collected the data in the form of electricity bills, family income, family size, the number of electric appliances, and energy use for lighting, cooking, heating and cooling from 442 households in Kalikot, Chitwan and Kathmandu districts of Nepal. The monthly rate of firewood use was found 2.08 GJ/household/month in rural areas, which is quite higher than other developing countries. The average electricity use of all areas in Nepal was 2.06 GJ/household/year, which is quite low compared to developed and even other developing countries. The electricity is used mainly for lighting purpose in the rural area, while it was also used to run other electric appliances in semi-urban and urban areas. By analyzing the data, it was found that the amount of electricity use was a function of household income level, occupation, family size and educational level of household responsible person. The educational level of people was correlated to the use of light-emitting diode lamps. The result of this series of survey should be useful for future planning of household energy management in the Nepalese society. [ABSTRACT FROM AUTHOR]

• The types and benefits of occupancy-based temperature controls for residential buildings are reviewed. • A systematic analysis approach is performed for a wide range of temperature controls specific to Saudi households. • Smart occupancy-controls can save on average 36% of annual energy consumption for a typical KSA household. The paper describes the potential energy efficiency and demand response benefits of occupancy-based temperature controls applicable to residential buildings for the Kingdom of Saudi Arabia (KSA). Specifically, various spatial and temporal temperature controls are evaluated to account for spatial and temporal occupancy patterns associated to typical living styles of Saudi households. The analysis results show that the benefits of occupancy-based temperature settings can be significant when at least two thermal zones controlled by different thermostats exist within the same housing unit. Specifically, occupancy-based thermostat settings can save up to 38.7% of total annual electricity consumption for a dwelling located in Riyadh. Moreover, when the indoor temperature settings are adjusted spatially, a reduction of up to 34.7% in peak demand can result for most KSA regions. Using a bottom-up analysis approach, it is found that when scaling up to the entire KSA housing stock, the adoption of smart thermostats has the potential to reduce annual electricity use by 48 TWh/year or over 30% of the national residential sector energy consumption. [ABSTRACT FROM AUTHOR]

In this paper we present new methods to collect and analyse household activity and electricity consumption data. We show how such data can be used to establish relationships between activities and electricity consumption in two different ways. First we create direct associations between activities and the electricity demand at the time. This electricity footprint gives an insight into the typical load intensity of an activity, which can include other activities that are commonly performed in close temporal proximity. Our novel contribution is the concept of marginal electricity demand which can be attributed to specific activities. To disaggregate and approximate the loads for such an activity, we present a new regression approach. The marginal demand of an activity is based on its relative load contribution to any other activity within a collection of over 18,000 activity pairs. Meal types stand out as clearly distinguishable activities. Hot meal related activities show some of the strongest positive contributions to demand, whereas cold meals tend to reduce demand at the time of reporting. Activities such as a hot drink can lead to an overall reduction in demand at the time, despite their direct energy consumption. The potential of this marginal metric will be discussed as a new way to understand energy service demands. Unlike conventional attribution of demand to appliances, it is possible for activities to have a negative marginal demand. This opens up new opportunities to conceptualise the meaning of energy service demand and approaches to encourage demand reduction or shifting through positive actions. [ABSTRACT FROM AUTHOR]