Residential Electricity Research Articles

Adversarial Dynamic Load-Altering Cyberattacks Against Peak Shaving Using Residential Electric Water Heaters

Adversarial Dynamic Load-Altering Cyberattacks Against Peak Shaving Using Residential Electric Water Heaters

A Mixed-Methods Approach for Evaluating the Influence of Residential Practices for Thermal Comfort on Electricity Consumption in Auroville, India

With rapid global economic growth and a rise in disposable household income, particularly within a progressively warming planet, the escalating demand for energy to achieve thermal comfort has become a salient concern in the Global South, notably in emerging economies like India. This burgeoning need for cooling solutions has not only underscored the vital role of energy consumption but has also accentuated the imperative of comprehending the ensuing implications for electricity policy and strategic planning, particularly within the ambit of the Global South. This study explored the nuanced landscape of active cooling within an intentional community, Auroville, in southern India, aiming to discern the factors underpinning household preferences and practices in the pursuit of thermal comfort. Employing a mixed-methods approach, this study contributed empirically and methodologically to the interdisciplinary discourse by analysing residential electricity consumption patterns and cooling practices within selected households in the specified community. The study unfolded in three methodological stages: firstly, an analysis of climatic data coupled with an environmental stress index (ESI) assessment; secondly, the monitoring of end-user electricity consumption followed by rigorous data analysis; and lastly, the utilisation of qualitative in-depth interviews and observational techniques. This study’s outcome yielded empirical insights into the unprecedented shifts in the ESI for Auroville since 2014. Furthermore, the study unravelled the intricate complexities inherent in occupant behaviour within residential structures, thereby offering valuable insights into the practices that shape householders’ cooling preferences. This research enriched the understanding of the dynamics of energy consumption in the pursuit of thermal comfort and contributes to the broader discourse on sustainable development and energy policy in the context of climate change.

Relationship between Socio-economic Factors and Electrical Energy Consumption of Customer Groups in Indonesia

The research aim was to study Socio-Economic Analysis and its Relationship with Electrical Energy Consumption of Customer Groups in Indonesia using secondary data from 2018-2022 covering 34 provinces in Indonesia. Data sources generally come from Central Bureau of Statistics (BPS) and State Electricity Company (PLN) Indonesia. The results of the analysis show that GDP per capita, education and MSMEs have a significant negative influence on poverty in Indonesia. Furthermore, GDP per capita is the only variable that has a significant positive effect on electricity consumption, while education, MSMEs and poverty have no effect on residential electricity consumption. In the industry group, all variables have a significant effect on household consumption, except for education which has an insignificant effect, GDP per capita and MSMEs have a significant positive effect on electricity consumption, apart from that, education and poverty also have an effect but are not significant in the Business group. The research results also show that poverty does not have a significant impact, but GDP per capita, education and MSMEs have a significant positive influence on social electricity consumption. Furthermore, in the Government Office Buildings (GOB) group, GDP per capita and MSMEs have a significant positive effect, however, education and poverty do not have an influence on electricity consumption in this group. Finally, in the Public Street Lighting (PSL) group, GDP per capita, MSMEs and poverty have no effect, but MSMEs have an influence on electricity consumption.

Income, extreme temperature, and residential electricity consumption in the Spanish Provinces

The effect of income and extreme temperatures on the residential electricity consumption in Spain is analyzed. An electricity demand function is estimated by means of ordinary least squares-Driscoll and Kraay, feasible generalized least squares, and quantiles panel data techniques, for the total of the sample, and by coastal climatic zones. The results support the energy-environmental Kuznets curve hypothesis for Spain, the continental, and Mediterranean zones. An N form is found for the Atlantic zone. The results also reflect that increases in temperature above 22∘C and decreases below 15∘C increase electricity consumption, this increase being progressive as temperatures vary. Thus, extreme temperature generates electricity consumption growth. The Mediterranean zone is most sensitive to these temperature changes. The results also show that electricity consumption is more sensitive to cold than to heat. The results suggest an inefficient use of heating and cooling appliances in the areas with the highest electricity consumption.

A Study of Households’ Perception towards Solar Panel Installation Case Study: Villagers of Techno-park nearby Faculty of Technology, University of Sri Jayewardenepura

In Sri Lanka, energy demand has been exponentially increasing with the outage of non-renewable energy sources. There is significant solar intensity in most of the areas in Sri Lanka. This study focused on the domestic solar power system since domestic solar panel usage (700 MW) is the country's highest contributor to solar energy usage. A photovoltaic (PV) system can supply electric energy to loads by directly converting solar energy through the photovoltaic effect with a flexible structure. This study aimed to analyze households' perceptions of solar panel installation. The study was conducted within a 5 km radius around the Faculty of Technology, the University of Sri Jayewardenepura, to analyze the awareness of solar-related technologies. A systematically developed questionnaire was used for the survey to collect the peoples' awareness based on environmental, social & economic beliefs. Thirty residential places (30) were interviewed. According to the results, a residential house's average monthly electricity consumption is 137.67 kWh. The mean monthly income in the area is sixty-eight thousand one hundred and sixty-six rupees and sixty-seven cents. Moreover, most people had solar energy knowledge (93.33%). Among them, 53.57% of the majority were willing to install Domestic Solar PV systems for residential places. From the consumers, 76.66% of the majority had an average level of awareness of the Environmental benefits of using renewable energy and 70.00% of the majority had an excellent awareness of government intervention in rooftop solar energy projects such as "Soorya Bala Sangramaya‖. From the defined awareness levels, further analysis was done on the lowest level of Awareness of Solar Energy to identify their characteristics. The majority of them were those who had lower energy consumption, were over the age of 60 and had a monthly income below twenty thousand rupees. According to the consumers, the best strategy for promoting Solar PV Systems was to lower the prices of existing Solar PV systems. Regulating the efficiency of the Solar PV system was identified by the existing Solar PV system owners. 
 Keywords: Solar PV system, Techno-park, Photovoltaic panels

Pattern-driven behaviour for demand-side management: An analysis of appliance use

Energy communities play a key role in the transition to sustainable energy, helping to inform and engage end-users so that they can become active energy consumers. In practice, trials and pilots often risk failure due to misplaced expectations and unforeseen behaviours when it comes to achieving flexible energy demand resources. In order to tackle these challenges, residential electricity load profile datasets and consumer survey results emerge as powerful tools for identifying controllable loads, energy consumption models, and tailored understanding of communities' energy contexts. This paper first outlines and analyses these datasets' capabilities to leverage data-driven decision-making for more efficient deployments of demand-side management (DSM) systems. A number of appliance behaviour patterns are extracted, based on high and flexible loads for shifting, being validated over three different use cases to support turn-key DSM in the presence and absence of renewable supply and bill saving. A genetic algorithm optimization is applied to underpin flexible demand reallocation and optimal community load profiles by combining time-variable tariff of use. Experiments demonstrate that controllable and shiftable appliances can reduce average peak load by up to 29% by increasing renewable self-consumption, leading to a valuable energy bill saving of 9%. Our findings also point to the current limitations of existing load/consumption datasets, which are hindering more efficient DSM design of flexibility and demand response programmes in energy communities.

Income elasticity of residential electricity consumption in rural South Africa

Analyses of residential electricity demand primarily rely on expenditure or aggregate data. However, newer sources of data, such as that from meter readings, are becoming available. In most circumstances, these newer sources cannot be matched to detailed information about the household. In this research, we make use of South Africa’s Domestic Electrical Load Study, one of the only sources available in a developing country that includes both meter and household level data. Due to some gaps in the meter readings, we focus our attention on average peak electricity consumption, estimating the income elasticity with respect to morning, evening, and the average across both morning and evening peaks. Although we find differences in income responsiveness in the morning, relative to the afternoon, and across quantiles of electricity consumption, these differences tend not to be statistically significant. We do, however, find heterogeneities in those elasticities that can be correlated with, in particular, appliance ownership, suggesting that the ownership of appliances makes electricity more of a necessity, or at least makes the services derived from electricity more necessary for the household.

Residential Electricity Consumption Prediction Method Based on Deep Learning and Federated Learning Under Cloud Edge Collaboration Architecture

Traditional residential electricity prediction methods have problems, such as difficulty in ensuring user privacy and poor convergence speed due to the influence of Different Residential Electricity Consumption (REC) habits. A REC prediction method based on Deep Learning (D-L) and Fed-L under the Cloud Edge Collaboration (CEC) architecture is proposed to address the above issues. First, based on the CEC architecture, combining edge computing and cloud computing center server, the overall model of REC prediction is built. Then, Federated Learning (Fed-L) and D-L model Empirical Mode Decomposition - Long Short-Term Memory (EMD-LSTM) were introduced on the edge side, and the edge side Fed-L depth model was personalized by using EMD-LSTM. Finally, aggregation of edge side models was achieved in the cloud by receiving encrypted model parameters from the edge side and updating and optimizing all edge side models. The results show that the proposed method has the highest REC prediction accuracy, reaching 96.56%, and its performance is superior to the other three comparative algorithms.

Optimizing energy consumption in smart homes: Load scheduling approaches

AbstractRising fuel prices, global warming, and environmental damage are leading to increased demand for rooftop solar energy systems connected to the power grid. The development of smart grids, modern metering systems, and energy management could promote energy conservation in households. In this study, two different meta‐heuristic optimization techniques were employed to schedule the shiftable load in suitable hours for decreasing electricity costs and minimizing peak to average ratio in a smart home while maintaining optimum user comfort. For power generation and storing energy, a grid‐connected residential load with rooftop solar panels, a battery, and an inverter is considered. First, the problem is theoretically described using a load model and an objective function, with the primary goal of reducing power costs by moving the time of usage for certain home appliances. Simulations validate the proposed strategies, which effectively reduce power costs by 4.5% by shifting the time of use, with both optimization algorithms showing similar output. The residential electricity cost before optimization was 507.12 BDT/day, which decreased to 484.33 BDT/day after optimization without compromising load turn‐off.

Planning for Heat Resilience and the Future of Residential Electricity Usage

Community resilience refers to the ability for a geographic area to respond and adapt to acute shocks and long-term stresses. Geography research is well positioned to examine how communities can adapt to the compounding effects of climate change. Our work aims to analyze the factors that influence residential electricity in the context of increasing urban temperatures in Maricopa County, Arizona, coupled with the COVID-19 pandemic. Using census tracts as our basis for analysis, we quantified factors that influence electricity use related to neighborhood characteristics, home structures, social situations, pricing policies, and work-from-home estimates. Our findings suggest that the structure of the home (e.g., number of rooms and house size) influences the variation in residential electricity use, whereas trees and small-area temperatures were not factors. We then compared above-median-income census tracts to those that were below the median income. We found fewer factors influenced electricity use in the lower income census tracts and that they were also related mostly to the home structure. Our analysis did not reveal that working from home was significant, but we did find that the average household size was significant and that the amount of influence increased from 2019 to 2020, suggesting that the stay-at-home policies from the pandemic did affect electricity use. As we consider and implement work-from-home strategies, families need to recognize that the home structure plays a crucial role in determining electricity usage.

Quantitative assessment of hydrogen blending in a gas turbine-based plant for supplying residential electricity, water, and cooling load: A techno-economic-environmental optimization

Heat recovery not only enhances the thermal efficiency of a thermodynamic system but also yields cost and environmental benefits. Additionally, utilizing produced hydrogen as a fuel source, when blended with natural gas, significantly diminishes CO2 emissions. However, a dearth of existing research in the literature that addresses the simultaneous integration of heat recovery and hydrogen blending prompted the authors to propose, design, and model a multi-generation energy system. This proposed system takes into account economic and environmental considerations while incorporating hydrogen blending and heat recovery techniques. The model was developed using MATLAB to accurately calculate the system's functions, and the resulting programming code was meticulously cross-checked against literature findings. To identify the optimal operating conditions for the system, a genetic algorithm optimization approach was employed. The base case results indicated a total cost rate of 7039.4 $/h, an exergy efficiency of 41.01%, and a drinkable water production rate of 830.65 kg/s. Moreover, the optimization results demonstrated improvements in various parameters, with a reduced total cost rate of 6833 $/h, an enhanced exergy efficiency of 49.46%, and an increased drinkable water production rate of 1166.4 kg/s. The introduction of hydrogen blending led to an 8.16% decline in the total cost rate, a 1.15% reduction in CO2 emissions, and a notable 4.6% enhancement in the overall system's exergy efficiency.

Application of dynamic models in forecasting the total population of the United States

Dynamic models have been widely cited in predicting criminal population, residential electricity consumption, food prices and other objects. However, for total population predictions, dynamic models are rarely used. In this study, we will analyse the relationship between 13 variables such as CPI, grain prices, and medical expenditures and the total population of the United States, then combine it with the ARIMA model to generate a time series dynamic regression model. The conclusion is that, according to the parameters of the final model, two predictors (CPI and the number of crimes) and one interaction term (the product of the poverty rate and unemployment rate) are significantly related to changes in the population. Ultimately, the model performed well on the test set and was remarkably accurate for population prediction five years later. This report screens various factors influencing the total population and provides a broader background for applying dynamic models. In addition, this study also provides directions for subsequent research on more efficient dynamic models.

China's local-level monthly residential electricity power consumption monitoring

Timely implementation of electricity power consumption (EPC) analysis and evaluation helps assess energy-saving potential and develop energy management plans. Yet, timely EPC data of subnational regions are usually public unavailable, especially for developing countries. Lacking timely and local EPC data makes it difficult to satisfy the requirements for local governments to quickly monitor energy consumption and establish early warning mechanisms. As a solution, this study establishes a quick accounting method for local residential EPC utilizing nighttime light data, based on which we investigate the variations in residential EPC before and after the COVID-19 pandemic. Our method has demonstrated robust performance in estimation and proven its reliability. Compared to previous studies, the proposed methodology has four advantages. First, it monitors local-level residential EPC data at a monthly scale, which allows for observing EPC statistics at a higher frequency. Second, it separates residential EPC from urban and rural sectors, which is useful for analyzing urban-rural dichotomy energy consumption patterns. Third, it provides a less costly and time-consuming approach to estimating energy consumption over space and time. Fourth, it covers almost the whole China (except Tibet, Hongkong, Macao, and Taiwan due to data unavailability), providing useful information for achieving national energy-saving goals. Based on the methodology, we see great potential for developing “global-country-region” energy monitoring through satellite image data service globally.

Techno-economic and environmental analysis of a grid-connected rooftop solar photovoltaic system in three climate zones

Abstract This study aims to fill a gap in research on technical-economic and environmental assessments of grid-connected photovoltaic (PV) panels for residential electricity supply. To combat this, a study examines the feasibility of grid-connected rooftop solar PV systems in three cities. Using PVsyst software, technical, economic, and environmental factors were analyzed, including energy injected into the grid, net present value (NPV), internal rate of return (IRR), levelized cost of energy (LCOE), and life cycle emissions. It is concluded from this study that PV power plants are technically, economically, and environmentally feasible for all three climate zones, but cold zone with the highest annual production of 10.66 MWh, the highest NPV ($5449.47), the highest IRR (14.28%), and the lowest LCOE ($0.063/kWh) is the most appropriate place to set up a PV power plant (Scenario 1: No tracking system). Furthermore, using horizontal (Scenario 2), vertical (Scenario 3), and two-axis tracking (Scenario 4) systems instead of the no-tracking system (Scenario 1) increases the amount of electricity injected into the grid by 7.56%, 24.67%, and 36.35%, respectively. According to life cycle emission, the annual production of 10.66 MWh by the 5 kW power plant installed in cold zone will prevent 102.02 tons of carbon dioxide emissions into the atmosphere (Scenario 1: No tracking system). To remove financial obstacles and increase the viability of renewable energy, the government must provide financial incentives, lower the cost of equipment, and enact strong renewable energy legislation.

Examining behavioral aspect of urban context effect on residential building energy use in Seoul – linking urban walkability to energy

Recent studies show that urban contextual form can influence building energy use intensity (EUI). While many studies have focused on the thermal properties of buildings and their environment, human behavior, a key determinant of EUI, is also influenced by the contextual form. Urban walkability, a key measure of urban vitality, can encourage outdoor activity and thus alter occupant behavior and outsource energy demand. This study examines the behavioral aspect of the context effect on residential building EUI in Seoul. Summer electricity, winter gas, annual electricity, and annual gas EUIs are individually analyzed. Two sets of analyses are executed for each energy type. First, walkability is used as a simple explanatory variable in an ordinary least squares regression model. Second, walkability is used as a moderator for the socioeconomic parameters of occupant density, occupant age, and real estate price. The results suggest that an increase in walkability can significantly reduce residential electricity EUI, and walkability can modify the influence of occupant density and occupant age on electricity EUI. However, walkability is only a marginally significant predictor of residential gas EUI, and all interactions with socioeconomic variables are insignificant. These findings highlight the importance of walkability as a means of linking urban form and building energy.

Occupant behavior, thermal environment, and appliance electricity use of a single-family apartment in China

The household is the basic unit of a residential community or building. High-resolution, long-term open data are necessary to help study residential electricity consumption, smart home technologies, and electricity flexibility technologies at household level. This paper introduces an IoT-based data collection platform (IDCP) consisting of gateways, sensors, and cloud servers. This platform can collect data on the occupant presence, indoor environment, window-opening states, and appliance electricity consumption simultaneously. This study deployed the IDCP in a single-family apartment in Beijing, China, and compiled a dataset, namely, CN-OBEE, including data on the per-minute occupant behavior, thermal environment, and appliance electricity use of the apartment for an entire year (from May 31, 2021, to May 31, 2022), and hourly meteorological data collected by the nearest national weather station during the same period. This dataset is the first detailed and publicly available occupant behavior and electricity use dataset for Chinese homes. As a regional feature, the dataset compiled by this study includes window-opening behavior and the use of split air conditioners (ACs).

Enhancing Residential Electricity Safety and Management: A Novel Non-Intrusive Load Monitoring-Based Methodology for Accurate Appliance Operational State Identification

Non-intrusive load monitoring (NILM) technology, crucial for intelligent electricity management, has gained considerable attention in residential electricity usage studies. NILM enables monitoring of total electrical current and voltage in homes, offering insights vital for enhancing safety and preventing domestic electrical accidents. Despite its importance, accurately discerning the operational status of appliances using non-intrusive methods remains a challenging area within this field. This paper presents a novel methodology that integrates an advanced clustering algorithm with a Bayesian network for the identification of appliance operational states. The approach involves capturing the electrical current signals during appliance operation via NILM, followed by their decomposition into odd harmonics. An enhanced clustering algorithm is then employed to ascertain the central coordinates of the signal clusters. Building upon this, a three-layer Bayesian network inference model, incorporating leak nodes, is developed. Within this model, harmonic signals are used as conditions for node activation. The operational states of the appliances are subsequently determined through probabilistic reasoning. The proposed method’s effectiveness is validated through a series of simulation experiments conducted in a laboratory environment. The results of these experiments (low mode 89.1%, medium mode 94.4%, high mode 92.0%, and 98.4% for combination) provide strong evidence of the method’s accuracy in inferring the operational status of household electrical appliances based on NILM technology.

A policy design perspective on electricity rates

Electricity rates affect citizens’ energy-related behaviors. In the past and present, studies of the behavioral influences of the designs of these rates have heavily focused on the structure and effects of pricing mechanisms. This price-centric perspective has, this paper demonstrates, led other crucial aspects of rate designs to be overlooked despite their potential implication for more efficient and equitable citizen energy access and climate change mitigation. To explore the composition of rate designs beyond the oft-examined pricing component, this study develops and utilizes a novel application of policy design scholarship to more comprehensively examine the rate designs that facilitate residential electricity provision and shape energy consumption behaviors. Through this approach, multiple design elements and more granular sub-elements are identified in contemporary rate designs. Furthermore, emergent properties of rate design diversity and complexity are discerned as design features occur individually and combinatorically. Ultimately, this study not only tractably expands the concept of rate designs, but also advances a method for examining the policy instruments through which public service provision occurs.

Joint Management and Optimization of Residential Natural Gas and Electricity Distribution Networks Coupled via Fuel Cells

Joint Management and Optimization of Residential Natural Gas and Electricity Distribution Networks Coupled via Fuel Cells

Construction of Electricity Load Forecasting Model Based on Electricity Data Analysis

Abstract This paper builds a time series prediction model of recurrent neural networks based on time series electricity load forecasting. In this paper, the household electricity consumption record data of some residents in urban area S is taken as the research object, and the laws and characteristics of users’ electricity consumption behavior are analyzed in depth based on the real residential electricity consumption data. External factors such as temperature conditions, holidays, etc. The arithmetic cases are also analyzed using real load data sets. In the short-term continuous electricity data analysis, the smaller the time interval is, the closer its corresponding electricity consumption ratio is to 1. There is a negative correlation between long-term continuous electricity consumption. When the temperature is 30~35oC versus -5~0oC, electricity consumption rises significantly. Comparing and analyzing the time series decomposition-RNN with several models, the time series decomposition-RNN model has the highest fit at 10:00-12:00 and 12:00-14:00, and the result verifies the validity of the model proposed in this paper.