Household Electricity Demand Research Articles

Assessment of agricultural residue potential for electrification of off-grid communities in the Sawla-Tuna-Kalba District of Ghana

BackgroundThere is a close link between the lack of electricity access and poverty indicators such as illiteracy, high infant mortality, lack of access to health care and malnutrition among others. Most rural farming communities in Ghana lack access to electricity due to the high cost of extending the grid to these communities. This lack of access tends to worsen the gap between urban and rural inhabitants regarding access to education, healthcare and development.MethodsThis study assessed the technical and theoretical potential of agricultural residues in providing electricity to off-grid communities. The study used crop production figures of maize, cassava, millet and groundnut in the Soma and Goyiri farming communities in the Sawla-Tuna-Kalba District to conduct an assessment of the theoretical and technical potential of residues from the crops. The production figures of these crops were obtained from the District Office of the Ministry of Food and Agriculture. Expected electricity demand of households, schools and health centers in the study communities were collected and employed for the projected load demand estimates.ResultsThe study found that 312.23 MWh/day of electricity could be generated from the combined residues of maize, cassava, millet and groundnut from the two communities. This amount of electricity is capable of providing ~ 202 to 263 times the peak electricity demand of the studied communities. Out of the total electricity demand of the two communities, only about 91 kWh/day is needed for use in a school and Community Health Promotion and Services (CHPS) compound, implying that the electricity from crop residues can also help to improve education and health provision in the rural communities.ConclusionIt is concluded that the potential of crop residues in meeting the electricity demand of off-grid communities is enormous. Hence, it must be considered in Ghana’s energy development plans to achieve universal electricity access.

Impacts of intermittent water supply on household electricity demand: An econometric analysis for the Pune Metropolitan Region, India

Private household water and energy use are closely linked, especially in areas of intermittent water supply where more than one billion people live globally. However, the demand-side Water-Energy Nexus at the household level is often overlooked in empirical econometric studies. Based on a household survey (n = 1872) on water and energy in the Pune Metropolitan Region, India, we find statistical relationships between intermittent water supply and household electricity demand. More than 90 % of the surveyed households use water storage to cope with water supply intermittency, low-income households are particularly affected. Electricity consumption for water access accounts for 27 % of total household electricity consumption. Using a Discrete-Continuous Choice model, we identify significant impacts from factors such as household size and income, electricity price, and particularly the duration of water supply and the use of large water storage on household electricity demand. Our results indicate that households with 24-h water access consume 30 % less electricity than those with 12-h daily access. Extending municipal piped water supply by 1 h per day for all households could reduce total household electricity consumption by 3 %. Our findings suggest that water supply intermittency is a massive cause of unnecessary emissions in cities around the world that has thus far received hardly any attention. The significant amount of electricity used to access water reveals a hidden water affordability problem that can be more prevalent during droughts. Our analyses highlight the demand-side Water-Energy Nexus from an econometric perspective and emphasize the importance of breaking down silos in resource management.

Electrical Demand Analysis on Households and Industry in Indonesia

This research aims to determine the response of the household sector and industrial sector to electricity demand when there are changes in prices and income. The influence of price and income on electricity demand from both sectors can be seen through their elasticity. The approach used in this study is panel data from 33 provinces in Indonesia for the 2010-2020 time period. Panel data regression estimation techniques are used in this study to estimate the elasticity value. The results show that price elasticity in the household and industrial sectors is negative inelastic, but price does not significantly influence household electricity demand. Unlike price, income elasticity has a much higher value and positively and significantly influences electricity demand in both sectors. The number of customers, which reflects the increasing electrification ratio and population growth, significantly impacts electricity demand in the household and industrial sectors. Based on the results, it was found that the number of customers most influences electricity demand in the household sector. At the same time, income has the most significant influence on electricity demand in the industrial sector.

Efficiency Analysis of the Photovoltaic Shading and Vertical Farming System by Employing the Artificial Neural Network (ANN) Method

Productive facades, consisting of photovoltaic shading and vertical farming systems, have been proposed as a means to improve the thermal and visual status of residential buildings while also maintaining energy performance and providing vegetables. However, how to quickly and accurately predict electricity and vegetable output during the numerous influencing architectural and environmental factors is one of the key issues in the early stages of design, and few studies have investigated the impact of such structures on both indoor environmental qualities and production performance. In this paper, we present a novel prediction method that uses experimental data to train and test an artificial neural network (ANN). The results indicated that using the Bipolar Sigmoid activation function to process the experimental data input to the artificial neuron network gives more accurate predicted results both in the yield of photovoltaic shading and vertical farming systems. In addition, this prediction method was applied to a typical high-rise residential building in Singapore to assess the self-sufficiency potential of high-rise residential buildings integrated with productive facades. The results indicated that the upper part of the building can meet 20.0–23.1% of the annual household electricity demand of a family of four in a four-room residential unit in Singapore and almost the entire year’s vegetable demand, while the middle part can meet 18.4–21.2% and 89.1%, respectively. The results demonstrated the importance of a productive facade in reducing energy demand, enhancing food security, and improving indoor visual and thermal comfort.

Driving change: Electric vehicle charging behavior and peak loading

Electric vehicles (EVs) are projected to comprise 40 % of Aotearoa New Zealand's light vehicle fleet by 2040. However, charging decisions made by EV drivers, such as whether to charge immediately or delay charging, will affect peak electricity demand and lifetime of distribution network components. This study uses an agent-based model (ABM) of EV charging to investigate the effect of different EV penetration levels and owner charging decisions on components in New Zealand's residential electricity networks, although the methodology is wholly generalizable to other countries or regions. Monte Carlo simulation is performed for EV charging in a neighborhood of 71 houses, based on a representative residential distribution network, and simulated for 20 days. The key outcome measure is the rate of ‘Exceedance’ of the 300 kVA baseline transformer limit, where greater Exceedance entails shorter lifecycle and increased maintenance or capital costs to the provider. Results show delayed-charging algorithms (‘Altruistic charging’) decrease peak electricity demand and Exceedance, while drivers charging immediately (‘Selfish charging’) increases Exceedance. New Zealand's residential electricity networks are expected to accommodate a 40 % EV transition with 100 % Altruistic charging, as Exceedance is expected to increase less than 20 % from Exceedance without EVs. However, Selfish charging increases the rate of Exceedance by more than 250 %. Longer-term, increasing EV penetration and household electricity demand will require increased workplace charging infrastructure, electricity network upgrades, and/or automated and Internet of Things (IoT)-enabled Demand Side Management (DSM) of EV charging to avoid high rates of Exceedance and increased maintenance and replacement costs.

Analysing the determinants of the Turkish household electricity consumption using gradient boosting regression tree

Residential buildings are the second largest electricity consumer in Turkey. Thus, the goal here is to detect the factors that determine the electricity consumption of the households in Turkey using the Household Budget Survey (HBS). This study applies Decision Tree (DT), Random Forest (RF) and Gradient Boosted Regression Tree (GBRT) methods. Since the GBRT method provides the lowest Root Mean Squared Error (RMSE), the impact of each variable on the electricity consumption is analysed with this method. The most critical determinant is found to be the household size, while income level and heating type are discovered as 2nd and 3rd most prominent determinants for household electricity demand. With the help of the Partial Dependence Plots (PDP) provided by the GBRT method, the impact of each categorical and continuous variable is presented. Using the results of PDPs, the monetary values of both electricity generation and the social cost of CO2 emissions emitted into the atmosphere due to electricity generation are calculated for the most important determinants.

Price and Income Elasticities of Hungarian Household Energy Demand: Implications for Energy Policy in the Context of the Energy Crisis

The energy crisis that began in the second half of 2021, exacerbated by the Russian-Ukrainian war, created unexpected difficulty for European Union Member States in terms of shaping their energy policies. Hungary was one of the most vulnerable countries, whose economic development was fraught with severe risk due to the strong dependence on Russian energy sources and regulated energy prices for households, significantly slowing down the transition to green energy. The sharp change in energy prices due to the introduction of a price cap in 2013-2014 and the partial adjustment in 2022 draws attention to the situation of Hungarian energy demand. This study describes the absolute short-term price elasticities of Hungarian household natural gas and electricity demand and gas and electricity cross-price and income elasticities of the former for income deciles between 2010 and 2021 using the midpoint percentage method. The results show that electricity demand is more elastic than gas demand, implying that Hungarian household consumers are more responsive to changes in electricity prices than those of gas. In addition, low-income Hungarian households are more sensitive to changes in both of the latter than high-income households, while no consistent pattern is identified in the relationship between income and energy demand. Accordingly, the study recommends the implementation of a multi-tariff pricing strategy based on the energy burden of the income deciles. This should particularly target the first two deciles, which are the most vulnerable and sensitive to energy price changes.

Energy planning in Sub-Saharan African countries needs to explicitly consider productive uses of electricity

Studies show the role of various electrification technologies in providing electricity access to households in Sub-Saharan Africa, with a focus on electricity demand for end-use services such as lighting, cooking, heating, cooling and other appliance use. The demand for productive use of electricity, which is important to enhance income generation opportunities and labour productivity, is usually not considered. Using the IMAGE-TIMER integrated assessment model framework, we present a methodology to project the impact of productive activities on the electricity system of the region. We show that growing productive activities increase household electricity demand by half, which has important consequences for determining the cost-optimal electrification technologies. We argue that planning of electricity systems should accommodate this increase in electricity demand for productive uses. In addition, while productive uses of electricity have a positive impact on the financial viability of electrification systems, they also increase the electricity sector investment requirements considerably.

The determinants of household electricity demand in Turkey: An implementation of the Heckman Sample Selection model

Residential areas have a significant share in electrical energy consumption in Turkey. At this point, determining electricity consumption demand in residential areas will significantly contribute to ensuring energy efficiency throughout the country. Accurate econometric modelling of the relationships between the various socio-economic characteristics of households and their energy consumption/expenditure can serve as a basis for policymakers when planning investments aimed at efficiency on primary energy consumption in residences. Considering these issues, this study aims to examine the electricity consumption demand in residential areas in Turkey in terms of various socio-economic and building and property characteristics variables. At this point, the fact that some households have zero electricity consumption has led to the need to use censored regression models. The Heckman Sample Selection model was used, which makes it possible to estimate the consumption decision of individuals in two stages and eliminates the parameter bias problem caused by linear regression analysis. Heckman Sample Selection modelling was carried out in the study using Turkish Statistical Institute (TURKSTAT) 2019 Household Budget Survey data. According to the results, it has been determined that annual disposable income, various demographic variables (age, education level, marital status), characteristics of the house (size) and the presence of various electrical household appliances (air conditioner, tumble dryer, deep freezer) have significant effects on electricity consumption. The systematic preparation of informative brochures on the effective use of household electrical appliances can significantly contribute to efficiency. In addition, in countries like Turkey that depend on foreign energy, providing incentives towards renewable energy sources is considered necessary to contribute to the country's economy positively.

Determinants of electricity consumption from decentralized solar PV mini-grids in rural East Africa: An econometric analysis

The use of decentralized solar photovoltaic (PV) mini-grids for rural electrification is increasing in developing countries, but little is known about the determinants of electricity consumption of communities electrified through these technologies. This paper examines the factors influencing the electricity consumption of rural households and small businesses electrified by off-grid PV mini-grids based on actual metered load data and a survey of 218 customers in two isolated small towns (Omorate and Tum) in Ethiopia. Empirical analyses were performed using Censored Tobit models. Results showed that the load curves of the two towns have different characteristics and patterns. While the load curve at Omorate is regularly interrupted for 13 h a day due to load-shedding, the mini-grid at Tum generates enough electricity to meet demand. Empirical results showed that electricity consumption of households is significantly associated with household size, income, dwelling type, number of rooms, cooling fans, cooking with electricity and load-shedding. In contrast, the electricity consumption of businesses is strongly linked with income, electricity price, number of rooms, number of cooling fans, refrigerators, number of other (productive use) appliances, and load-shedding/location. The findings suggest three key points. First, electricity demand of rural households and businesses is influenced by separate but interconnected sets of factors. Second, supply-side factors, appliance factors, type of end-use of electricity and location -specific factors influence demand more than income and price factors. Third, mini-grid policy making and dimensioning in rural East Africa must take into account the differing electricity demands and determinants across customer groups and locations.

Time-series clustering and forecasting household electricity demand using smart meter data

This study forecasts electricity consumption in a smart grid environment. We present a bottom-up prediction method using a combination of forecasting values based on time-series clustering using advanced metering infrastructure (AMI) data, one of the core smart grid technologies. Remote data metering every 15 min to 1 h is possible with real-time communication on power generation information, consumption, and AMI development. Hence, its prediction is more challenging due to the large variation of each household’s electricity. These issues were solved by time-series clustering methods using Euclidean distances and Dynamic Time Warping distance. The auto-regressive integrated moving average (ARIMA), ARIMA exogenous (ARIMAX), double seasonal Holt–Winters (DSHW), trigonometric, Box–Cox transform, autoregressive moving average errors, trend and seasonal components (TBATS), neural network nonlinear autoregressive (NNAR), and nonlinear autoregressive exogenous (NARX) models were used for demand forecasting based on clustering. The result showed that the time-series clustering method performed better than that using the total amount of electricity demand regarding the mean absolute percentage error and root mean squared error.Hence, various exogenous variables were considered to improve model accuracy. The model considering exogenous variables—cooling degree day, humidity, insolation, indicator variables, and generation power consumption performed better than that without exogenous variables.

The impact of China’s fertility policies on the future electricity demand of residential households

Under China’s “comprehensive two-child” policy (UTCP) and “three-child” policy (TCP), household’s age structure is bound to change, and this change will further have an impact on household demand for electricity. In order to accurately forecast the impact of fertility policy on residential household electricity demand, we firstly construct a prediction model with annual household electricity consumption as the dependent variable and average electricity price and household-level old-age dependency ratio (HODR) as the independent variables. Second, we use the cohort-component projection model (CCPM) to construct a population prediction model to obtain the macrolevel old-age dependency ratio (MODR). Third, based on the interactive ritual chain (IRC) theory, we explore the statistical relationship between macrolevel old-age dependency ratio and household-level old-age dependency ratio to obtain a prediction method for the household-level old-age dependency ratio. Finally, we take five households as examples and predict their electricity demand from 2022–2035. The following conclusions are obtained: 1) the average electricity price and the household-level old-age dependency ratio have a significant suppressive effect on the electricity demand; 2) for households without the pressure of old-age dependency, the Under China’s “comprehensive two-child” policy’s contribution to electricity demand is pregnant, while “three-child” policy’s is limited; 3) the Under China’s “comprehensive two-child” policy and “three-child” policy cannot fundamentally curb the declining trend of residential electricity demand of China in the long term.

ИСПОЛЬЗОВАНИЕ ЭВОЛЮЦИОННОГО ГЕНЕТИЧЕСКОГО АЛГОРИТМА ДЛЯ РАСЧЕТА ЦЕН НА ЭЛЕКТРОЭНЕРГИЮ ДЛЯ КОНЕЧНЫХ ПОТРЕБИТЕЛЕЙ

Proposal of a new method of pricing for electricity. Household electricity demand is project-ed based on their past usage patterns. Users are grouped into different groups based on their demand, and then an evolutionary algorithm is used to generate prices for users present in different groups in real time.

A day-ahead prediction method for high-resolution electricity consumption in residential units

In order to accurately predict the day-ahead of household electric demand, a day-ahead high-resolution prediction model termed temporal-behavior coalescing forecast is proposed for the household energy demand. This model considers both behavioral and temporal dependencies. Feature extraction and data decomposition techniques are used to construct the behavioral and temporal inputs for the proposed model to reduce the negative impact of invalid data on its performance. In the proposed model the behavioral feature step and temporal feature step are constructed based on the convolutional network and long short-term memory network. Particularly, a coalescing step is designed in end the proposed model to strive for model convergence and enables the model to process two input matrices of different dimensions simultaneously. A 15-min resolution residential building energy demand dataset is used to validate the proposed model. The accuracy and generality of the proposed method are increased by 20.69% and 25.28%, respectively, compared with other related models. The validity of the proposed model is verified. In the robustness experimental, the proposed model can still maintain excellent prediction performance with the large noise introduced. A basis for its reproducibility and engineering application is provided.

PV-EV integrated home energy management using vehicle-to-home (V2H) technology and household occupant behaviors

The Vehicle-to-Home (V2H) topic is considered a promising and potential field of research due to its ability to address a variety of issues related to smart household electricity consumption and consumer energy demand. Therefore, as the population grows and their habits change, so does the need for domestic energy. Consequently, the provision of electricity poses increasingly difficult problems. To this end, the Home Energy Management System (HEMS) is being implemented to avoid similar problems. To maximize the utilization of various home appliances under energy demand and a predetermined timetable, we propose a precise Home Centralized Photovoltaic (HOCP) System, which includes V2H technology, Solar Photovoltaic (SPV), and a Green Electric Vehicle (GEV). The suggested design is aimed at reducing residential energy demand by providing for optimal automation of appliances. The recommended HOCP design will cover household electricity demand under sunny and rainy conditions without requiring additional electricity. On the other hand, the combination of electricity transmission SPV and V2H is sufficient to meet the demand for residential load in cloudy and sunny conditions. In this vein, HEMS with various constraints is implemented to perform proper system operations and satisfy load demand. The proposed algorithm attempts to characterize and perceive the situation of sources through the use of energy demand. The results show that H2V in combination with a renewable energy source can effectively reduce energy use and future residential applications during peak periods.

Assessment of the wind power plants role in CO2 emissions reducing and energy supply in the regions of Russia

The article contains an analysis of the share of mayor wind power plants in regional energy mixes and evaluation of their contribution to CO2 emission reducing, basing on the data for wind capacities construction and launch. The estimations showed that wind plants in Republic of Kalmykia could cover more than 94% of industrial and household electricity demand. Forecast evaluations for the wind plants planned to be put into operation until 2024 demonstrate a high wind potential for satisfaction of energy demand in Republics of Kalmykia and Adygeya as well as in Ulyanovsk and Astrakhan regions. The decrease in CO2 emissions in the regions of Russia was estimated by 2024 due to the absence of emissions at wind farms in the amount of 4.5 million tons per year.

The Relationship between Electricity Prices and Household Welfare in South Africa

The study examines the relationship between electricity prices and household welfare in South Africa. The study employs a demand system framework on annual time-series data from 2000 to 2018 and the analysis involves the calculation of price elasticities and measurement of welfare changes. The price elasticities in this study are drawn from the linear expenditure demand model. To analyse welfare change, we consider the impact of electricity pricing policies on cost of living (proxied by the consumer price index and households’ expenditure patterns). The study achieves this: (i) by comparing electricity price movements to changes in the rate of inflation between 2000 and 2018; (ii) by regressing total household energy expenditure against household expenditure on electricity, to examine how electricity costs affect a household’s overall energy bills; and (iii) thirdly, by regressing household food expenditure against households’ electricity expenditure to determine how the latter affects a household’s ability to spend on other basic goods and services. The results of the study show: (i) South African household electricity demand is inelastic to changes in price of electricity; (ii) electricity prices in the country increased at a higher rate than the rate of inflation for most of the time during the study period, suggesting that households incurred increased expenditures to achieve their desired utility or satisfy their energy needs during this period; (iii) household total electricity expenditure is positively related to household total energy expenditure, implying that high household expenditure on electricity exerts upward pressure on the overall household energy budgets; and (iv) household total food expenditure is negatively related to household total energy expenditure. This shows that while policy makers achieved significant success with providing physical access to electricity, affordable access to this basic service is still a concern and affects the overall welfare of households in the country. The study recommends a review of the country’s electricity tariff structure to make affordability a key objective. Moreover, the study calls for coordinated efforts in addressing Eskom challenges which have also played a contributing role to the current energy crisis, characterized by an unreliable electricity supply and constantly increasing electricity prices.

FAKTOR-FAKTOR YANG MEMPENGARUHI PERMINTAAN KONSUMEN TERHADAP LISTRIK PADA RUMAH TANGGA

This research aims to determine the factors that influence the family's electrical energy demand. The research method used is descriptive quantitative, the data used is secondary data in the form of community income, population and economic growth. The results showed that income had a significant positive effect on household electrical energy demand, population had a significant positive effect on household electrical energy demand and economic growth had no effect on household electrical energy demand. Simultaneous test results show that all independent variables have an effect of 95.7% on household electrical energy demand.

A deep reinforced learning spatiotemporal energy demand estimation system using deep learning and electricity demand monitoring data

Tracking decarbonization effects requires a model for the identification of spatial energy demands on city facilities. However, most developing countries lack detailed discrete time and device-specific energy demand data. In this study, we installed multiple energy demand monitoring systems that could observe electricity demands at the device level in some residences in Bogor, Indonesia. The study aimed to estimate the time-series and equipment ratio of electricity consumption by households in the entire city based on the monitoring data. However, the number of households monitored was small, and therefore unlikely to be regarded as having a representative system. Therefore, we used questionnaire data to create monitored mimic data and increased the number of samples to estimate the energy demand characteristics of the entire city via spatial interpolation. In addition, we developed a reinforcement learning system for discrete time–electricity demand estimation systems for unmonitored households using a 5-step procedure; 1) Analyzing energy demand and its patterns from monitoring data, 2) Questionnaire-based surveying of households, 3) Estimation of energy demand and its patterns based on questionnaire responses in monitored households, 4) Development of a deep learning model that extends the results from (3) to unmonitored households using data fusion, and 5) Spatial interpolation of energy demand characteristics for all households in Bogor using a spatial statistics method. The spatial electricity demand of households was interpolated from GIS and high-resolution satellite data matching procedures. Based on this analysis, we developed an hourly energy demand prediction system that could be automatically improved by adding new data from the reinforced learning framework.

Forecasting household electricity demand with hybrid machine learning-based methods: Effects of residents’ psychological preferences and calendar variables

Faced with the explosive growth of population and rapid development of industrialization, household electricity demand (HED) has recently become an essential sector of energy demand. Very-short-term forecasting of HED is closely related to monitoring insecurity, detecting emergency and potential crisis. Consider challenges triggered by the characteristics of time series, firstly, a phase of data pre-processing with stationarity of time series is constructed to manage irregular trend factors. Subsequently, in the feature selection phase, a two-layer feature selection method is proposed with the combination of random forest and autocorrelation analysis to select the most relevant features, which simultaneously considers correlations and importance of input lagged variables as well as exogenous variables. After that, hybrid HED forecasting methods are constructed with incorporating multiple machine learning techniques in the forecasting phase, namely k-nearest neighbor, support vector regression, random forest and multilayer perceptron, using their respective advantages of managing different situations. As residents’ psychological preferences and calendar variables highly affect future HED, this paper intends to add and test their effects on HED. Finally, the proposed methods are examined on a 10-min-ahead horizon application, whose forecasting results, comparison analyses and an extended experiment are given to illustrate their effectiveness, after which some implications are provided to show the insights gained from the results.