Domestic Electricity Consumption Research Articles

Estimation of Domestic Load Profile for Effective Demand-Side Management

In this article domestic base and shift based load profile are estimated for effective Demand Side Management (DSM). Domestic electricity consumption in Pakistan accounts for approximately 40% of total electricity sales. Effective DSM can reduce electricity consumption. Power generation companies are increasingly recognising the importance of analysing their customers' consumption patterns. This article proposes an agent-based modelling approach for forecasting household electricity consumption profile. AnyLogic software is used for establishing the model that uses variables and functions to estimate base and shiftable load of three hundred households. This article also estimates the weekly and 24- hour load profile for effective DSM operations by smart grid infrastructure. The results show that the agent-based simulation closely approximates real-time data, with a difference of only 2.4%.

Predicting UK Domestic Electricity and Gas Consumption between Differing Demographic Household Compositions

This paper examines the influence of building characteristics, occupant demographics and behaviour on gas and electricity consumption, differentiating between family groups; homes with children; homes with elderly; and homes without either. Both regression and Lasso regression analyses are used to analyse data from a 2019 UK-based survey of 4358homes (n = 1576 with children, n = 436 with elderly, n = 2330 without either). Three models (building, occupants, behaviour) were tested against electricity and gas consumption for each group. Results indicated that homes without children or elderly consumed the least energy. Property Type emerged as the strongest predictor in the Building Model (except for homes with elderly), while Current Energy Efficiency was less significant, particularly for homes with elderly occupants. Homeownership and number of occupants were the most influential factors in the Occupants Model, though this pattern did not hold for homes with elderly. Many occupant and behaviour variables are often considered ‘unregulated energy’ in calculations such as SAP and are thus typically disregarded. However, this study found these variables to be significant, especially as national standards improve. The findings suggest that incorporating occupant behaviour into energy modelling could help reduce the energy performance gap.

Performance of vertically mounted bifacial photovoltaics under the physical influence of low-rise residential environment in high-latitude locations

This study focuses on finding suitable installation sites for vertical bifacial photovoltaic (VBPV) panels in urban low-rise neighborhoods at high latitudes. The power production of east-west-oriented VBPV systems matches well with domestic electricity consumption profiles, increasing the self-consumption of PV electricity. Furthermore, PV electricity adds economic value by avoiding transmission fees and taxes. These systems are especially beneficial in high-latitude locations characterized by a low solar elevation angle. However, these low angles expose VBPV panels to a high risk of shading losses from their surroundings, and it is unknown how much shading limits the number of suitable installation sites. Here, environmental shading on VBPV panels is quantified for three low-rise residential neighborhoods in Helsinki, a high-latitude location, by comparing the specific yields (annual electricity production per kilowatt peak) of VBPV and monofacial PV (MPV) systems. The results showed that unshaded VBPV systems have a higher specific yield than their MPV counterparts. However, in densely built neighborhoods with tall trees, the lack of suitable installation sites for VBPV panels severely limits the peak power of these systems. Roof ridge VBPVs usually yield high production, while façade- and ground-mounted systems lose between 30% and 70% compared to roof ridge VBPV systems depending on their installation locations. South-oriented MPVs perform better than VBPVs on north-south-facing roofs, both in terms of specific yield and total annual production. Conversely, VBPVs installed on the ridges of unshaded roofs aligned closely with the north-south axis outperform MPVs on east- and west-facing roofs by 20%–30%.

A novel structure adaptive discrete grey Bernoulli prediction model and its applications in energy consumption and production

It is well known that energy forecasts play an important role in guiding energy policy, economic development and technological progress. Therefore, based on the purpose of energy consumption and production forecasting, this paper proposes a novel structure adaptive discrete grey Bernoulli model, which is innovative in terms of both accumulated generating operator and model structure. In terms of accumulated generating operator, a new fractional order accumulated generating operator is proposed in this paper. The new accumulated generating operator has a different information priority by adjusting the values of the parameters. In terms of model structure, a novel discrete grey Bernoulli model is proposed in this paper. The novel model is well adapted to time series data containing nonlinear information, and can well mine and utilize the information contained in the original data. In addition, the Particle Swarm Optimization (PSO) algorithm was chosen to optimize the model parameters based on algorithm comparison experiments. This enables the model to flexibly adapt to a variety of complex data and has the ability of structure adaptive. Moreover, this paper conducts comparative experiments between the novel model and eight other forecasting algorithms for time series data. The numerical results show that the novel model has better forecasting performance for the data of China’s total energy consumption, China’s total electricity generation and China’s total domestic electricity consumption. In addition, for the model reliability problem caused by the optimization algorithm, the stability and accuracy of the model are verified by Monte Carlo simulation and probability density visualization analysis. Finally, the proposed model predicts the future development trend of energy consumption and production in China.

Tracing the Path to Sustainability: A Study on Domestic Electricity Consumption, Historical overview of Renewable energy resources, transitioning to sustainable energy sources and its impact on Health

Modern economies are centered on electricity, which is also contributing to an increasing amount of energy services. As a result of increased household incomes, electrification of transport and rising demand for digital connected devices and air conditioning, there will be a rise in the need for energy. Residential electricity consumption in India has tripled over the past few decades with houses having access to uninterrupted power supply. This study is conducted in order to understand the determinants of electricity consumption such as income, family size, stock of electrical appliances, size of dwellings and their influence on consumer’s electricity consumption. It also seeks to examine if the consumers are willing to reduce their electricity consumption and are interested in shifting to renewable energy resources considering the costs involved in shifting to more sustainable energies. Due to the nature of power, which is invisible from the moment it was discovered, as well as consumer attitudes and behavior, saving electricity may appear to be a challenging undertaking. However, if the consumer acquires a basic understanding of what electricity is, how it is utilized, and, more significantly, how energy is wasted, the problem might become very simple. This will make consumers more aware of wasteful electricity use and perhaps encourage them to modify their daily consumption of electricity.

An optimization in big data time series prediction method by Parzen estimation with Spark

With the development and change of big data related technologies, more and more large amounts of data need to be analyzed. Now there are companies like Google, Yahoo, etc. Frameworks such as MapReduce, Hadoop, Spark, etc. are developed for processing large amounts of data. In this paper, relevant discussions and researches are carried out on time series forecasting under the new era of big data. Now there are time series forecasting methods based on map reduce, Hadoop, spark data processing framework, including nearest neighbor distribution method, neural network method, etc., which have made quite good achievements in big data time series forecasting. By reading the relevant research literature, it is universally acknowledged that the Sparks framework has good application prospects and potential in predicting big data time series. As a result, this paper is mainly aimed at the optimization and improvement of the big data time series forecasting method on the basis of the spark framework. The author noticed that most of the default configurations of spark clusters are generated by default or automatically, rather than the optimal solution obtained after algorithm optimization, so there is still room for improvement in this regard. In this regard, this paper proposes a kernel method for visual data processing of related configurations and parameters, and then optimizes the default data configuration as much as possible to improve the accuracy and feasibility of the big data time series prediction method on the basis of the spark framework. In this paper, the optimized scheme is used to forecast the domestic electricity consumption in the past five years, and the results show that the optimized scheme has a good improvement performance on the basis of the original method.

Regional Heterogeneity Analysis of Residential Electricity Consumption in Chinese Cities: Based on Spatial Measurement Models

The share of electricity consumption by urban and rural residents in China’s overall electricity consumption is very close to that of the tertiary sector, which has become an important driver of China’s electricity consumption growth. At the same time, due to the mismatch between China’s regional resource endowments and the level of regional development, the regional supply and demand situation for electricity varies. Therefore, it is urgent to clarify the regional differences in residential electricity consumption and the factors affecting it, and accordingly adopt targeted and feasible measures to regulate residential electricity consumption. This article includes data from 285 Chinese prefecture-level cities from 2006 to 2019, and adopts a “three lines” method of region-partitioning (Qinling–Huaihe line, Huhuanyong line, and Shanhaiguan line) to divide four regions. We used spatial econometric models to examine residential electricity consumption and its influencing factors in China from the standpoint of regional heterogeneity. The results show that there is significant regional heterogeneity in residential electricity consumption in China, and the difference between the north of the Shanhaiguan line and other areas is significant. Moreover, there is a positive spatial correlation in the per capita domestic electricity consumption of urban residents, and each influencing factor has obvious regional heterogeneity, among which household appliances are the significant influencing factor. Based on the regional heterogeneity of residential electricity consumption, management measures should be formulated according to local conditions, and the supply of electricity should be ensured by strengthening multidimensional initiatives.

Forecasting of Domestic Electricity Consumption in Assam, India

Though the power supply scenario in Assam is not comparable with other developed states of India, yet it is improving gradually. With increasing household electrification rate, increasing income and technological development, the share of Domestic electricity consumption in the state is experiencing a rapid growth in comparison to the other sectors like Industry, Commerce, Agriculture etc. and therefore the forecasting of domestic electricity consumption become very important to meet the power needs of the consumer. The aim of this study is to suggest the best forecasting model for domestic electricity consumption in Assam. For this purpose, three models viz. Multiple Linear Regression considering the effects of population and per capita income; ARIMA and Growth curves viz Linear, Quadratic, Cubic, Exponential, Logarithmic and Inverse are used considering the time period 1980-2018. When growth curves are compared in terms of diagnostic criteria- Adjusted R2, RMSE, AME and MAPE, then it is found that exponential model fits the data better than the other growth models. After that, comparisons are made among the models Multiple Linear Regression, ARIMA and Exponential by considering their average relative error and found the efficiency of ARIMA the highest followed by Exponential and Multiple Linear Regression models. Therefore, forecasting of domestic electricity consumption in Assam has been done with ARIMA model for the next 10 years.

What factors drive the temporal-spatial differences of electricity consumption in the Yangtze River Delta region of China

Effective control of electricity consumption is crucial to achieving China's dual carbon goals, as it can significantly reduce coal consumption and associated carbon emissions. Using the Logarithmic Mean Divisa Index (LMDI) method, this paper constructs a two-stage decomposition model, which can divide total electricity consumption into industry and domestic electricity consumption and further examine the differential driving mechanism of the two types of electricity consumption separately. Taking the Yangtze River Delta as the research object, this paper analyzes the drivers of its temporal differences in total electricity consumption from 2000 to 2019 and fills the research gap in the identification of drivers of spatial differences in electricity consumption. The results showed: (1) Industry electricity consumption is the main driver of the increase in total electricity consumption, with economic growth and domestic electricity intensity being the primary and secondary factors behind this trend. The population scale also contributes to the increase. However, the effects of industrial structure, industry electricity intensity, and urbanization on total electricity consumption are not constant over time. This paper further decomposes the factors influencing changes in total electricity consumption in the provinces of Shanghai, Jiangsu, Zhejiang, and Anhui. (2) The spatial difference of electricity consumption between the other three provinces and Shanghai mainly comes from industry electricity consumption. (3) There are two kinds of conversion relations for the spatial-temporal differences of total electricity consumption. According to the conclusion, the targeted countermeasures and suggestions for total electricity consumption control are obtained.

Domestic electricity consumers and the interactive bill: design intervention for improved energy literacy and empowerment

Domestic electricity consumption has been the subject of numerous interventions that address environmental, economic, social, and technical issues, with the primary objective of reducing the consumed kilowatt per hour (kWh). Taking a mixed-method approach, this research explores a design intervention through a prototype of a digital interactive electricity bill. This innovative bill had a dual purpose: 1. A design probe for improving usability, usefulness, and user experience; and 2. A research tool for generating knowledge and understanding of the perceptions, attitudes, and behaviors of domestic electricity consumers. This three-phase study involved domestic electricity consumers, who explored and interacted with the prototype. Findings show that the interactive bill is more understandable and useful compared to the current printed or e-document bill, and thus may enhance users' empowerment, sense of control, curiosity to seek additional information and energy literacy. The study provides a direction for further research, toward establishing a connection between design and smarter energy consumption.

INNOVATIVE SOLUTIONS FOR ENSURING ENERGY SECURITY OF UKRAINE AND THE WORLD

The article examines the preconditions for the aggravation of the energy danger for Ukraine and the world as a whole, in particular in the context of the strengthening of the economic crisis and war in Ukraine. The issue of compliance by energy organizations with the International Climate Paris Agreement and the achievement of the Global Sustainable Development Goals by 2030 were studied. The component indicators of the global index of energy innovations were analyzed. The main global energy indicators and their results in 2021 compared to 2020 were monitored. The paper also estimates domestic electricity consumption in the world by country in 2021, as well as trends in the growth of global energy consumption in general and by continent in 2021 compared to 2020. The result of such trends was that the consequences of sanitary measures and the economic crisis of 2020 was felt mainly in the sphere of services, transport and carbon-intensive electricity production. The authors considered the key changes in the field of energy in the world in 2021 compared to 2020 and the dynamics of CO2 emissions related to energy in the G20 countries in recent years and based on this justified the forecast of European gas prices and the price of Brent oil to 2026 year. The main characteristics of countries with a high index of global energy innovation were considered. It was found that, for example, the Norwegian government had developed a strategy for carbon capture and storage (CCS), which aimed to identify measures to promote technology development and reduce the costs of CCS. It was found that the main goal of the EU regarding the share of renewable energy sources is 32% of the final energy consumption by 2030. This target was not distributed among the Member States, but the share of renewable energy sources in the Member States should be at least the same as in 2020. In the article, the authors examined the stages of state regulation of the innovation policy of the energy sector of Ukraine and substantiated the place of the energy sector in the national innovation policy of Ukraine. Formation of innovation policy in the energy sector of Ukraine requires definition of the concept and strategic plans of its innovative development. In particular, it was found that one of these priority areas should be renewable energy (wind, solar, etc.). The result of the authors’ scientific research is that the innovative system of the energy sector is a fairly developed network intellectual structure that connects research and design organizations belonging to different sectors of the economy. Using the potential of the positive influence of a number of important global factors should be the basis of the transformation of innovative management of energy industry organizations in Ukraine.

Methodological Principles of Planning the Development of Mongolia’s Electric Power System

The article addresses matters concerned with the methodological features of planning the development of the electric power system (EPS) of Mongolia, the concepts laid down in the documents defining the development of Mongolia’s energy sector, and some results of studies on working out the country’s EPS development model. The EPS management and planning problems, which follow from the need to improve the energy security indicators and matching it with the growth of domestic electricity consumption and the features of the external market, have been defined, and possible options for a unified electric power system have been proposed based on the systems analysis method in comparison with the mathematical modeling method.

A Digital Twin Approach to City Block Renovation Using RES Technologies

The building sector accounts for over 40% of global energy consumption, and many buildings are old and inefficient. However, the current pace of building renovation is not sufficient to make a tangible impact. A new strategy is needed to accelerate the renovation process. Renovation at the district level and the use of digital tools, such as a digital twin (DT) of a city district, can provide a solution. This paper proposes a novel approach to city block renovation using renewable energy sources (RES), including photovoltaic (PV) solar panels, heat pumps (HP), and electric heaters (EH), while utilizing a DT of a city district to provide a user-friendly representation of the results and data needed for holistic solutions. The proposed method combines an optimization model of the optimal heating system with a solar PV simulation technique to analyse hybrid RES solutions and potential on-site energy generation and supply. Several scenarios are simulated to evaluate RES solutions in the renovation process of the city block using the DT concept. The simulation results demonstrate that a hybrid RES solution, which includes a PV system and a heating system, is optimal when the on-site generated energy is used not only for domestic electricity consumption, but also for the operation of HPs and EHs for heat generation. This study highlights the importance and significance of a DT approach to city block renovation and provides a new solution to accelerate the renovation process and reduce energy consumption in the building sector.

Evaluation of surplus hydroelectricity potential in Nepal until 2040 and its use for hydrogen production via electrolysis

The abundant hydro resources in Nepal have resulted in the generation of electricity almost exclusively from hydropower plants. Several hydropower plants are also currently under construction. There is no doubt that the surplus electricity will be significantly high in the coming years. Given the previous trend in electricity consumption, it will be a challenge to maximize the use of surplus electricity. In this work, the potential solutions to maximize the use of this surplus electricity have been analysed. Three approached are proposed: (i) increasing domestic electricity consumption by shifting the other energy use sectors to electricity, (ii) cross-border export of electricity, and (iii) conversion of electricity to hydrogen via electrolysis. The current state of energy demand and supply patterns in the country are presented. Future monthly demand forecasts and surplus electricity projections have been made. The hydrogen that can be produced with the surplus electricity via electrolysis is determined and an economic assessment is carried out for the produced hydrogen. The analysis of levelized cost of hydrogen (LCOH) under different scenarios resulted values ranging from 3.8 €/kg to 4.5 €/kg.

A pattern changes evaluation on household greenhouses gases during pandemic in Surakarta

The pandemic has caused a change in activity patterns that affect energy consumption and waste production due to restriction of activities. The purpose of this study is to find the changes in household GHG emission pattern during the pandemic in Surakarta Municipality. The focus of household GHG calculated in the study is electricity and liquid petroleum gas (LPG) consumption, waste production, and household batik industry. Greenhouse gases calculated with IPCC Guideline. Data activity used reference based approach. The total of household activity emission during the pandemic in Surakarta reaches 1035680.40 tonnes CO2eq, contributing to 33.5% of the total emission. The number comes from electricity consumption that produced 900709.38 tonnes CO2eq/year, LPG consumption that produced 107494.09 tonnes CO2eq/year, waste that produced 26050.44 tonnes CO2eq/year, and household batik industry that produced 1427.55 tonnes CO2eq/year. Monthly electricity consumption had 10% increase during the pandemic, surpassing the pre-pandemic highest value of 5.71%. LPG consumption had a decrease of 4.86%. Domestic waste production has an increasing trend of 36%, mostly containing inorganic waste (plastic). Pandemic created a new pattern on domestic electricity consumption.

Research on the factors influencing the growth of residential electricity consumption

Influenced by the continuous economic and social development and frequent occurrence of extreme weather, the factors influencing the growth of residential domestic electricity consumption in China are increasing, the mechanism of influence is more complex and the trend judgment is more difficult to judge. To this end, this paper studies the influence mechanism of residential domestic electricity consumption growth, researches the driving force of residential domestic electricity consumption growth, carries out correlation and regression analysis of factors influencing residential domestic electricity consumption growth, and draws the following conclusions: the number of air conditioners per 100 households has the strongest influence on per capita residential domestic electricity consumption, followed by the influence of per capita disposable income, and the influence of urbanization rate and population is relatively weak.

Urban domestic electricity consumption in relation to households’ lifestyles and energy behaviours in Burkina Faso: Findings from a large-scale, city-wide household survey

Information on actual domestic electricity use in Burkina Faso, where the urban zones and residential sector account for 74% and 33% respectively of the total electricity use, remains very challenging to find, as little research currently exists. This study aimed therefore, to provide the first ever insights into the actual urban residential electricity use. A survey with 387 households, the first large-scale, city-wide household electricity study undertaken in Burkina Faso to the authors’ knowledge, was conducted in the city of Ouagadougou. Information on the households' characteristics and behaviours were collected to yield a first comprehensive analysis on the actual city-scale domestic electricity use. Findings demonstrated an average electricity use of 2395 kWh/year by households. Cooling accounts for almost 40% of the total domestic electricity use, followed mainly by cooking and food preserving (23%) and information-communication-entertainment (19%) activities. Three groups of consumers were then formed based on their electricity use level, to investigate interactions between electricity use and households’ lifestyles. The study’s findings lay therefore, the foundations for a better understanding of actual urban domestic electricity use patterns and could help to develop more suitable policies and actions targeting energy conservation in the residential sector.

Impacts of Tariffs on Energy Conscious Behavior with Respect to Household Attributes in Saudi Arabia

Historically, the combination of generous subsidies along with extreme climate has led to unsustainable domestic electricity consumption in Saudi Arabia. The residential sector constitutes a significant portion of this consumption. Amid the economic challenges, the country enforced a new electricity tariff for residential consumers in 2018. This study thus leverages change in 2018–2020 by collecting and analyzing the electricity consumption data of 73 households in the Eastern Province of Saudi Arabia. The energy consumption is modeled based on the households’ attributes (e.g., dwelling type, ownership, number of residents, rooms, ventilation type, etc.) and applied tariffs using a machine learning technique. The extreme learning machine (ELM) is employed in solving the overfitting problem due to low-volume data. The correlation matrix is also constructed to determine the relationship between the household attributes. The ELM model developed in this study extracts the correlation between the input variables in determining energy consumption and also predicts the energy consumption related to low consumption data. The findings indicated that the electricity consumption between the pre-revised tariff year and the revised tariff enforcement year saw a reduction which was consistent in the subsequent years. This was also validated by the paired sample t-test, which showed a significant decrease in electricity consumption for the study period. The analysis also revealed that several household attributes had a relatively high impact on the reduction in the electricity consumption level following the revised tariffs, whereas the majority of the attributes had a moderate impact. In addition to these key findings, the demonstrated pathway adopted in this study is itself a methodological contribution that provides critical information about the sensitivity of the impacts of tariffs on energy consumption with respect to different household attributes. Economic factors being the critical stress need to be blended with existing energy consciousness for positive changes in favor of energy-saving behavior of the household members. The study does not attempt to represent the population of concern, but demonstrates a methodology that would help unleash inherent energy consciousness in favor of sustainable and energy-efficient behavior.

Optimal grid expansion under future electricity demand for groundwater irrigation in Ethiopia

Ethiopia is a lower-income country with an agrarian based, fast-growing economy that is susceptible to climate change impacts. As the nation continues to develop, domestic electricity consumption and electricity export opportunities will increase alongside a growing electricity demand for groundwater irrigation to mitigate the effects of drought. There are upcoming mega-dams– the Grand Ethiopian Renaissance Dam, Genale Dawa III, and Koysha – that will triple the electricity generation capacity. However, the central electricity grid is not sufficient to efficiently utilize this new capacity. We model future grid expansion scenarios by building a regional-scale linear optimal power flow model based on a transshipment representation of power flow that guides cost-optimal generation operations and transmission expansion, while satisfying competing electricity demand for domestic electricity consumption, groundwater irrigation, and international electricity export.We first evaluate cost-optimal generation and transmission expansion for a baseline case, with an estimate of current Ethiopian electricity demand. We then consider five potential future demand scenarios. These demand scenarios include estimates of both present and future domestic electricity demand, multiple levels of productive electricity demand for groundwater irrigation, and demand for international exports. Compared to the baseline, seasonal capacity factors for generators in the five model scenarios increased when collocated with an increase in electricity demand except for the generators that already operated at maximum capacity. All scenarios with higher domestic regional demand than the baseline surpass the transmission expansion planned by the Ethiopian Electric Power Corporation, independent of changes in irrigation and export demand. Our analysis suggests that the current expansion plan is not sufficient to meet future demand. Our research contributes to the literature by creating an optimal power flow model that can be applied in data-poor environments to model endogenous electricity infrastructure decisions under different scenarios of internal and external demand.

Avoiding Overconfidence in Predictions of Residential Energy Demand Through Identification of the Persistence Forecast Effect

Forecasting domestic electricity consumption is important for a wide range of modern power system solutions and smart applications that support network operation, grid stability, and demand-side management, most of which depend on robust and accurate predictions. The methods producing these predictions infer future load from statistical regularity in historical data. If such regularity is lacking, predictions then regress towards the most recently observed consumption value used in the input set. Predictions then follow the actual load data one step behind in time, potentially affecting the robustness of predictions and functionality of applications. Current evaluation methods do not detect this behaviour which may result in overconfidence in prediction results. In this study, we I) define and systematically analyse this behaviour, which we label the Persistence Forecast Effect and illustrate its impacts, II) propose a novel method, called 1-Step-Shifting, to detect its presence, and III) analyse and establish the relationship between irregularity in data and the effect. Further, we provide a case study applying state-of-the-art forecasting techniques to a real-world dataset of electricity consumption data from 69 households in order to demonstrate the Persistence Forecast Effect, its implications, and its relationship to statistical regularity in historical data.