Electricity Consumption In Residential Buildings Research Articles

Drivers and spatial patterns of carbon emissions from residential buildings: An empirical analysis of Fuzhou city (China)

Global economic development will continue to harm the natural environment. China is the largest contributor to global carbon emissions, with cities and the construction sector playing a major role. Understanding carbon emissions from buildings in cities can inform mitigation measures. This study conducted a comprehensive accounting of carbon emissions from lighting and appliance electricity consumption in residential buildings. The investigation covered Fuzhou city in Fujian Province of China in 2011–2021. ArcGIS generated and analyzed the spatial patterns of carbon emissions by districts. The STIRPAT model identified the leading carbon-emission drivers. The findings indicate: (1) fine-grained spatial distribution of carbon emissions demonstrates a marked concentration in the city center and contiguous districts, low emissions in surrounding districts, and a gradually increasing trajectory; and (2) GDP per capita, urbanization rate, and resident population are the principal drivers of carbon emissions, with every 1 % increase in GDP per capita raising carbon emissions by 0.66 %. Relevant practical energy-saving and emission-reduction measures are distilled from the results. The findings provide a scientific basis for decision-makers to formulate emission-reduction targets and strategies for Fuzhou's residential buildings, a theoretical basis for promoting regional low-carbon development, and a reference for other regions with a similar developing economy.

Geospatial assessment of the carbon footprint of water and electricity consumption in residential buildings in Doha, Qatar

The process of estimating the carbon footprint (CF) has become a key method for managing greenhouse gas (GHG) emissions, guiding strategies for emission reduction and validating those strategies. Given the complexity of quantifying total lifecycle emissions in residential buildings, this study delves into assessing the CF focusing on water and electricity consumption in two types of residential buildings: mainly residential villas and residential flats. This assessment was carried out in Doha City, Qatar, using data on water and electricity consumption from 2017 to 2020. The study employs the Multi-Regional Input-Output Life Cycle Assessment (MRIO-LCA) model to calculate and convert the water and electricity consumption data into the CF of these buildings. Further, the study employs various methods for statistical and spatial statistical analysis of CF emissions, including geographically weighted regression (GWR), Ordinary Least Squares (OLS), and hotspot and cold spot assessments. The annual electricity CF emissions from these buildings are approximately 7 MtCO2 equivalent, with residential villas contributing about 83% of this total. Concurrently, the annual average water CF in residential buildings is around 0.06 MtCO2 equivalent, predominantly attributed to villas. The findings highlight the substantial impact of residential structures, particularly villas, on the city's overall CF emissions. Furthermore, the findings underscore the significant impact of residential structures, especially villas, on Doha's CF emissions, revealing a marked seasonal increase, particularly during the summer months and a notable spike in 2020. The spatial analysis of CF emissions reveals consistent spatial clustering patterns across different seasons in 2020. Elevated CF emissions from villas are concentrated in Doha's central, northern, and northeastern regions, while cold spots are predominantly in the eastern and southern areas. Understanding CF in residential settings is crucial for developing strategies to reduce emissions, enhance energy efficiency, and address climate change. This research helps inform targeted interventions for more sustainable residential energy and water use, aligning with broader environmental and climate goals.

Passive Building Energy Saving: Building Envelope Retrofitting Measures to Reduce Cooling Requirements for a Residential Building in an Arid Climate

In arid climates, a significant portion of the urban peak energy demand is dedicated to cooling and air-conditioning during the summer. The rapid urbanization rates in developing countries, particularly in the Gulf Cooperation Council (GCC), have intensified the pressure on energy resources to meet the indoor comfort needs of residents. As a result, there has been a substantial increase in energy demand, with a 2.3% rise recorded in 2018. Electricity consumption in residential buildings accounted for over 48.6% of the total electricity consumption. The choice of building fabrics used in a residential building can significantly impact the building’s passive performance and carbon footprint. This study aimed to enhance our understanding of how specific fabric details influence cooling energy usage in arid climates. To achieve this, a validation simulation model was initially created as a base case for a residential housing typology in Al Ain, UAE. This was followed by a parametric energy evaluation of various building envelope features. The evaluation was based on the reduction of yearly cooling load energy. The simulation results indicate that incorporating 50 mm of expanded polystyrene insulation into the outside walls significantly reduced energy consumption for cooling requirements in the arid UAE climate. Furthermore, no substantial difference was observed in the various roofing choices, including cool and green roofs, gravels, and sand roofs. Additionally, we concluded that the total solar energy transmittance (g-value) of windows played a more significant role than thermal transmittance (U-value) in reducing solar heat gain within the spaces. These findings should guide strategic decisions on building envelope upgrading for sustainable societies.

Development of a Machine Learning Framework Based on Occupant-Related Parameters to Predict Residential Electricity Consumption in the Hot and Humid Climate

Occupant-related variables constitute one of the most significant groups of factors influencing residential building energy consumption. However, prediction methods often oversimplify these parameters, leading to substantial discrepancies between predicted and actual consumption. To address this issue, the present study aims to develop a machine learning framework for predicting electricity consumption in residential buildings based on occupant-related factors. The study incorporates twenty-six inputs, including occupant characteristics such as demographics, occupancy, behavior, and behavioral efficiency, two time-related factors, and three extra parameters related to equipment (refrigerator age, hot water source, and type of electricity meter) for training and testing the Random Forest (RF) algorithm in both regression and classification forms. The results indicate that the trained RF regressor exhibits well performance (R2Train = 0.989, R2Test = 0.916, MAETrain = 0.81, MAETest = 2.21, RMSETrain = 1.27, and RMSETest = 3.45). Furthermore, feature importance analysis reveals that the most significant parameter is the time of year, representing weather conditions, followed by the number of occupants, neighborhood, indoor set-point range, mean age of occupants, window opening, and cooling system mode. Even after removing the least impactful factors, the model maintains strong performance with the 16 most important variables (R2Train = 0.986, R2Test = 0.910, MAETrain = 0.83, MAETest = 2.25, RMSETrain = 1.31, and RMSETest = 3.49). Additionally, the RF classifier is designed for problems with 2, 4, 6, and 8 classes based on energy consumption ranges. The results of this model demonstrate that the 2-class model achieves the highest performance (AccuracyTest = 0.963, MAETest = 0.04, and RMSETest = 0.19). However, it lacks detailed categorization of homes based on electricity consumption. On the other hand, the 4-class and 6-class models strike a good balance between prediction performance and the level of detail. In conclusion, the proposed method can accurately predict residential electricity consumption and can serve as a valuable reference for researchers and utility managers when formulating energy reduction policies and comparing the effectiveness of different strategies.

The influence of information privacy concerns and perceived electricity usage habits on the usage intention of advanced metering infrastructure

According to the increase in electricity consumption in residential buildings, advanced metering infrastructures (AMIs) have been widely installed in residential buildings. AMIs help households to monitor and reduce their electricity consumption. Although the usefulness of AMIs has been validated, there still exist several barriers to their widespread use. This study considers household’s information privacy concerns (IPCs) and perceived electricity usage habits (PEUHs) as barriers to AMI penetration. AMI data on electricity usage may reveal extensive information about households, such as indoor behaviors and appliance types. This information may cause households to be concerned about invasion of privacy, and such concerns may affect AMI usage intentions. In terms of PEUHs, electric power companies argue that households with undesirable electricity usage habits, such as large amount of electricity usage with unpredictable usage patterns, are prospective AMI customers. This study develops IPCs and PEUHs scales and conducts a path analysis based on the framework of technology acceptance model to validate the influence of IPCs and PEUHs on AMI usage intention. The identified effects of IPCs and PEUHs on AMI usage intention are expected to provide practical information for understanding how IPCs and PEUHs influence AMI usage intention from the perspective of households.

How do households perceive electricity consumption? Evidence from smart metering and survey data in South Korea

As electricity consumption in residential buildings has increased, advanced metering infrastructures (AMIs) have been widely installed in residential buildings. AMIs measure and collect metering data of electricity (AMI data) consumed in each household. Companies, such as electricity suppliers and service and policy developers, have used AMI data to develop various services and policies. They rely on actual electricity consumption from AMI data to understand households' needs. However, the actual consumption may be different from household's perceived consumption. Household's perceived consumption could be affected by their respective characteristics, such as demographic and psycho-social characteristics. Accordingly, this research examines a difference between household's actual and perceived electricity consumption and determines household characteristics that affect the difference. For achieving this goal, this research uses AMI data (actual consumption) and survey data (perceived consumption) from 142 households in South Korea. Results confirm the existence of the difference and the influence of specific household characteristics on the difference between actual and perceived electricity consumptions. House and household sizes affect actual consumption, whereas behaviors that interact with electrical appliances affect perceived consumption. The findings would be useful for the companies in developing services and policies that satisfy household requirements.

Motivation, preference, socioeconomic, and building features: New paradigm of analyzing electricity consumption in residential buildings

In strategic energy planning, human-oriented factors are uncertain and lead to unpredictable challenges. Thus, decision-makers must contextualize the target society to address these uncertainties. More precisely, uncertainties lead to performance gaps between assumed and actual sustainability target outcomes. This study proposed a new framework that considers vital elements, including occupant motivation, preference, socioeconomic characteristics, and building features (MPSEB). To utilize this model, a thorough face-to-face survey questionnaire was administered to measure these elements. This study explored how these elements affect the patterns of residential energy consumption in a region with numerous expat communities of various ethnic and cultural backgrounds. In particular, the study investigated the patterns of energy behaviors and human-building interactions among the residents of Qatar by collecting empirical evidence and conducting a subsequent survey analysis. Machine learning approaches were employed to explore the survey data and determine the interdependencies between features, as well as the significance of the fundamental factors influencing human-building interactions. The XGBoost method was used to conduct a feature importance analysis to determine factors contributing to residential energy consumption. The results revealed the primary behavioral and socioeconomic factors that affect residential energy consumption, and confirmed the influence of human factors in Qatar while considering its diverse population.

Adaptive model predictive control for electricity management in the household sector

This paper focuses on the optimisation of electricity consumption in residential buildings. To deal with the increase in electricity consumption, the intermittency of renewable energy generation and grid contingencies, a greater effort is required towards residential management optimisation. A novel adaptive model predictive control algorithm is proposed to achieve this objective. The challenges for this research included recognising and modelling the economic and technical constraints of the sources and appliances and addressing the uncertainties concerning the weather and user behaviour. Data-driven models are developed and trained to predict the user behaviour and buildings. Artificial neural networks and statistical models based on the weighted moving average are proposed to capture the patterns of deferrable and non-deferrable appliances, battery storage, electric vehicles, photovoltaic modules, buildings and grid connections. A dual optimisation method is devised to minimise the electricity bill and achieve thermal comfort. The proposed optimisation solver is a two-step optimisation method based on genetic algorithm and mixed integer linear programming. A comprehensive simulation study was carried out to reveal the effectiveness of the proposed method through a set of simulation scenarios. The results of the quantitative analysis undertaken as part of this study show the effectiveness of the proposed algorithm towards reducing electricity charges and improving grid elasticity.

Deep Reinforcement Learning for Autonomous Water Heater Control

Electric water heaters represent 14% of the electricity consumption in residential buildings. An average household in the United States (U.S.) spends about USD 400–600 (0.45 ¢/L–0.68 ¢/L) on water heating every year. In this context, water heaters are often considered as a valuable asset for Demand Response (DR) and building energy management system (BEMS) applications. To this end, this study proposes a model-free deep reinforcement learning (RL) approach that aims to minimize the electricity cost of a water heater under a time-of-use (TOU) electricity pricing policy by only using standard DR commands. In this approach, a set of RL agents, with different look ahead periods, were trained using the deep Q-networks (DQN) algorithm and their performance was tested on an unseen pair of price and hot water usage profiles. The testing results showed that the RL agents can help save electricity cost in the range of 19% to 35% compared to the baseline operation without causing any discomfort to end users. Additionally, the RL agents outperformed rule-based and model predictive control (MPC)-based controllers and achieved comparable performance to optimization-based control.

RETRACTED: Techno-economic performance analysis in a nanogrid with Hybrid Renewable Energy Sources using classical optimization technique

Optimization algorithms are tools used in the planning and operations of renewable energy-based distributed power systems. Mixed integer linear programming as a classical optimization method is considered in the literature for sizing nanogrid systems due to simplicity and speed. However, the method has limited capabilities in implementing multi-configurational analysis and requires large formulations. In this paper, nested integer linear programming is proposed to decompose the large formulations and simplify the multi-configurational sizing of residential nanogrid in a semiarid zone. The proposed method is aimed at optimal sizing for energy cost reduction and increased supply availability. The method is implemented in multi-stage hybridization of relaxation and integer methods of linear programming to achieve optimal sizes of the nanogrid components using photovoltaics, wind turbines, and battery. The method realizes $72,343 net present cost and 0.3755 $/kWh levelized cost of energy indicating 33% and 11% reductions compared to mixed integer linear programming and particle swarm optimization. System availability of 99.97% is envisaged to achieve 6677–7782 kWh per capita electricity consumption in residential buildings against the existing 150 kWh. Three configurations analysed to indicate the robustness of the proposed method had multi-configurational designs clarify options against factors of space, logistics, or policies. • Nested integer linear programming algorithm is proposed for nanogrid sizing. • The method improves the reference algorithm through multi-configurational designs. • Levelized costs of energy were reduced by 7.7–39.7% in the three configurations. • The net present costs were reduced by 11.2–46.4% in two configurations. • The method optimizes the nanogrid’s capacity by 54.8–68.1%.

Multi-configurational sizing and analysis in a nanogrid using nested integer linear programming

Optimization algorithms are tools used in the planning and operations of renewable energy-based distributed power systems. Mixed integer linear programming as a classical optimization method is considered in the literature for sizing nanogrid systems due to simplicity and speed. However, the method has limited capabilities in implementing multi-configurational analysis and requires large formulations. In this paper, nested integer linear programming is proposed to decompose the large formulations and simplify the multi-configurational sizing of residential nanogrid in a semiarid zone. The proposed method is aimed at optimal sizing for energy cost reduction and increased supply availability. The method is implemented in multi-stage hybridization of relaxation and integer methods of linear programming to achieve optimal sizes of the nanogrid components using photovoltaics, wind turbines, and battery. The method realizes $72,343 net present cost and 0.3755 $/kWh levelized cost of energy indicating 33% and 11% reductions compared to mixed integer linear programming and particle swarm optimization. System availability of 99.97% is envisaged to achieve 6677–7782 kWh per capita electricity consumption in residential buildings against the existing 150 kWh. Three configurations analyzed indicated the robustness of the proposed method and the multi-configurational designs clarify options against factors such as space, logistics, or policies.

Nonintrusive Residential Electricity Load Decomposition Based on Transfer Learning

Monitoring electricity consumption in residential buildings is an important way to help reduce energy usage. Nonintrusive load monitoring is a technique to separate the total electrical load of a single household into specific appliance loads. This problem is difficult because we aim to extract the energy consumption of each appliance by only using the total electrical load. Deep transfer learning is expected to solve this problem. This paper proposes a deep neural network model based on an attention mechanism. This model improves the traditional sequence-to-sequence model with a time-embedding layer and an attention layer so that it can be better applied in nonintrusive load monitoring. In particular, the improved model abandons the recurrent neural network structure and shortens the training time, which means it is more appropriate for use in model pretraining with large datasets. To verify the validity of the model, we selected three open datasets and compared them with the current leading model. The results show that transfer learning can effectively improve the prediction ability of the model, and the model proposed in this study has a better performance than the most advanced available model.

Towards the Establishment of a Robust Load Forecasting Mechanism in Tanzania Grid: A Study to Investigate Effect of Air Temperature and Daytime on Electricity Consumption in Residential Buildings

- The current phenomenon of electric power management is towards the adoption of smart grid technologies to achieve efficient utility management processes such as transmission and distribution. Electric load forecasting has become an important aspect of smart grid technologies due to its capability of anticipating the power demand of a particular domain. The effective design of any load forecasting mechanism requires a critical investigation of electricity consumption determinants, following the diversification of social, economic, meteorological, and demographic grounds. Many research works have attempted to investigate the effect of temperature and daytime on electricity usage, targeting a specific country. The works have reported the existence of differing degrees of causality based on a particular country under investigation. The variation in findings from different research works has been a motivation to establish the study to examine the impact of air temperature and day time on load consumption in Tanzania. Four-years load and weather data have been collected from Tanzania Electric Supply Company (TANESCO) and Tanzania Meteorological Agency (TMA) respectively. The k-mean algorithm is used to detect outliers and missing values in the load dataset before further processing. Furthermore, the Shapiro-Wilk normality test method is applied to identify data distribution patterns which in turn leads us to the correct choice for Spearman’s rank correlation method. Results indicate that there is no linear relationship between electricity consumption and air temperature in residential buildings. Finally, findings indicate the existence of a strong causality degree between electricity consumption and day time in Tanzania.

Increase in domestic electricity consumption from particulate air pollution

Accurate assessment of environmental externalities of particulate air pollution is crucial to the design and evaluation of environmental policies. Current evaluations mainly focus on direct damages resulting from exposure, missing indirect co-damages that occur through interactions among the externalities, human behaviours and technologies. Our study provides an empirical assessment of such co-damages using customer-level daily and hourly electricity data of a large sample of residential and commercial consumers in Arizona, United States. We use an instrumental variable panel regression approach and find that particulate matter air pollution increases electricity consumption in residential buildings as well as in retail and recreation service industries. Air pollution also reduces the actual electricity generated by distributed-solar panels. Lower-income and minority ethnic groups are disproportionally impacted by air pollution and pay higher electricity bills associated with pollution avoidance, stressing the importance of incorporating the consideration of environmental justice in energy policy-making. While electricity production has a significant impact on air quality, the opposite effect has been suspected but not empirically demonstrated. Now, using pollution and electricity consumption data, He et al. show the impact of air pollution on domestic electricity consumption.

Critical factors of electricity consumption in residential buildings: An analysis from the point of occupant characteristics view

Occupant characteristics are critical factors that have a significant impact on electricity consumption in residential buildings, which have attracted increasing attention in the research field of building energy. This study analyses the influencing of occupant characteristic-related factors on electricity consumption in residential buildings with the consideration of different targets of electricity consumption, particularly by developing a statistical model based on a large data set published by the US Department of Energy. Results show that ECRB can be substantially affected by occupant characteristic-related factors, including ‘age of householders’, ‘household size’, ‘income’, ‘education level’, ‘type of occupancy’ and ‘length of residency’. They could explain 10.70% of the variance in electricity consumption. In which, type of occupancy’ has the largest impact, followed by ‘education level’, ‘length of residency’, ‘household size’ and ‘income’. It is also found that there are differences between some occupant characteristics corresponding to different occupant behaviors. Additionally, this study conducted an in-depth analysis of the relationship between occupant behaviour (including the use of energy star appliances, frequency appliances used, energy audit and insulation adding) and occupant characteristic-related factors. The analysis method serves as an effective tool for quantitatively evaluating the effect of various occupant characteristics in occupant behaviour modelling and building energy simulation, offering valuable references for practitioners, such that the appropriate strategies for electricity consumption saving can be selected in advanced.

Improvement of inverse change-point modeling of electricity consumption in residential buildings across multiple climate zones

Inverse modeling is a common method to predict electricity consumption in buildings. Residential building electricity consumption patterns can vary significantly due to occupants and their sporadic energy-consuming behaviors, as well as due to variations in HVAC system types and characteristics across climate zones. However most data-driven methods in this area have been developed and evaluated using limited datasets. This points to the need for an understanding of how well data-driven models perform using residential energy consumption data from a range of locations and home types using a diverse dataset. Thus in this research, first, inverse change-point modeling methods are used to develop predictive models of monthly electricity use for 3,643 houses in four U.S. cities in three ASHRAE climate zones (2A, 4A, 5A), to evaluate the model performance. However, approximately 40% of homes did not fit within recommended criteria for change-point model development following a common model development sequence. Therefore, a modified version of the sequence, including a segmented change-point model, is then developed and evaluated. Change-point models with relaxed prerequisite criteria are also used to enable the fitting of models to a larger number of homes’ data. As a result of these modifications, the number of homes with models increased from 60% to 71%, with a goodness-of-fit improvement of 13% (RMSE) and 8% (CV-RMSE) across the datasets evaluated. The results of this work enable improved prediction of energy use across a diversity of buildings and climate zones.

Analytical Methodology of Monthly Residential Building Electricity Consumption Based on Data Mining Models

With the development of urbanization, the energy consumption of residential buildings is growing rapidly. Previous researches mainly focus on the daily profile of residential electricity consumption, while monthly electricity consumption profile is essential for the design and optimization of power system and energy storage system. With the advancement of smart meters as well as mass data storage and transfer techniques, the availability of building energy data is growing rapidly. Thus, data mining methods are becoming possible and effective in the analysis of monthly electricity consumption in residential buildings. This paper first reviews the current research of monthly energy consumption profiles in residential buildings, then proposes a new stochastic distribution method based on data-driven models. A case study of Jiangsu residential building electricity consumption is conducted to illustrate and verify the application of this method. Finally, this paper discussed the future promotion of this method and possible perspective of monthly energy profiles.

Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks

This paper presents a recurrent neural network model to make medium-to-long term predictions, i.e. time horizon of ⩾1 week, of electricity consumption profiles in commercial and residential buildings at one-hour resolution. Residential and commercial buildings are responsible for a significant fraction of the overall energy consumption in the U.S. With advances in sensors and smart technologies, there is a need for medium to long-term prediction of electricity consumption in residential and commercial buildings at hourly intervals to support decision making pertaining to operations, demand response strategies, and installation of distributed generation systems. The modeler may have limited access to information about building’s schedules and equipment, making data-driven machine learning models attractive. The energy consumption data that is available may also contain blocks of missing data, making time-series predictions difficult. Thus, the main objectives of this paper are: (a) Develop and optimize novel deep recurrent neural network (RNN) models aimed at medium to long term electric load prediction at one-hour resolution; (b) Analyze the relative performance of the model for different types of electricity consumption patterns; and (c) Use the deep NN to perform imputation on an electricity consumption dataset containing segments of missing values. The proposed models were used to predict hourly electricity consumption for the Public Safety Building in Salt Lake City, Utah, and for aggregated hourly electricity consumption in residential buildings in Austin, Texas. For predicting the commercial building’s load profiles, the proposed RNN sequence-to-sequence models generally correspond to lower relative error when compared with the conventional multi-layered perceptron neural network. For predicting aggregate electricity consumption in residential buildings, the proposed model generally does not provide gains in accuracy compared to the multi-layered perceptron model.

Understanding electricity consumption: A comparative contribution of building factors, socio-demographics, appliances, behaviours and attitudes

This paper tests to what extent different types of variables (building factors, socio-demographics, appliance ownership and use, attitudes and self-reported behaviours) explain annualized electricity consumption in residential buildings with gas-fuelled space and water heating. It then shows which individual variables have the highest explanatory power. In contrast to many other studies, the study recognizes the problem of multicollinearity between predictors in regression analysis and uses Lasso regression to address this issue.Using data from a sample of 845 English households collected in 2011/12, a comparison of four separate regression models showed that a model with the predictors of appliance ownership and use, including lighting, explained the largest share, 34%, of variability in electricity consumption. Socio-demographic variables on their own explained about 21% of the variability in electricity consumption with household size the most important predictor. Building variables only played a small role, presumably because heating energy consumption is not included, with only building size being a significant predictor. Self-reported energy-related behaviour, opinions about climate change and ‘green lifestyle’ were negligible. A combined model, encompassing all predictors, explained only 39% of all variability (adjusted R2=34%), i.e. adding little above an appliance and lighting model only. Appliance variables together with household size and dwelling size were consistently significant predictors of energy consumption.The study highlights that when attempting to explain English household non-heating electricity consumption that variables directly influenced by people in the household have the strongest predictive power when taken together.

Balancing Fluctuating Renewable Energy Generation Using Cogeneration and Heat Pump Systems

AbstractFuture energy supply will depend increasingly on fluctuating renewable energy generation. This requires new techniques and methods to deal with fluctuations in the grid. Thermal energy systems with thermal storage capacities linked to the electrical grid, for example, combined heat and power (CHP) and heat pump systems can provide additional and necessary storage capacities. This study examines the potential of CHP electricity generation and heat pump electricity consumption in residential buildings to balance fluctuations of renewable energy generation in Germany’s future energy system. The optimal operation strategy is derived using mixed integer linear programming. The results show that CHP and heat pump systems can shift electrical energy to balance the residual load. The positive and negative peak load are reduced by 14 % and 33 %, respectively, and the surplus energy generation decreases by 59 %. An increase in thermal storage capacity reinforces these effects.