Electricity Consumption Demand Research Articles

Time series forecasting of electricity consumption using hybrid model of recurrent neural networks and genetic algorithms

The forceful energy efficiency to manage the demand is essential to meet development goals. Palestine has suffered from an electricity deficit, whereas the city of Tulkarm suffers from a chronic one. The dataset was collected from Tulkarm city in Palestine; this city is considered one of the cities that suffers the most from frequent power outages. It's difficult to determine the most powerful Artificial intelligence (AI) approaches that can accurately forecast electricity consumption. This paper presents a hybrid model that combines Recurrence Neural Networks (RNNs) and Genetic Algorithms (GAs) [RNN-GAs] to forecast electricity consumption and optimize demand. In the proposed model the K-means clustering technique produces specific initial population seeding and optimization crossover operators to enhance the efficiency and find the optimal solution. The results showed that the proposed Nonlinear Autoregressive with External (Exogenous) (NARX) (NARX-GAs) with the K-means clustering technique outperforms the hybrid model NARX-GAs. The NARX-GAs-K Mean Clustering recorded an RMSE value of 0.08759, which performs a good balance with the lowest RMSE, especially in long-term forecasting, and also outperforms the other hybrid forecasting models that depend on RNN-GAs. Finally, the forecasting results of the hybrid NARX-GAs-K Mean Clustering can predict accurately the energy consumption in a city, which leads to the use of the model in similar cities to forecast and manage the demand for electricity consumption.

Research on operational characteristics of coal power centrifugal fans at off-design working conditions based on flap-angle adjustment

The coal power centrifugal fan (CF) is one of the most significant equipment in coal power plants, which usually works at off-design conditions according to the demand of electricity consumption. Consequently, the efficiency of CFs is far lower than the designed maximum efficiency under this condition, causing large amounts of potential energy wasted. A novel flap-angle adjustment (FAA) method is proposed to efficiently solve this problem in the CF system. This manuscript establishes a theoretical model of the flap-angle adjustable centrifugal fan (FAACF). The operating economy of FAACF at both design and off-design conditions is comprehensively studied. The internal flow mechanism of efficient operation is analyzed, which reveals the more stable flow state inside CF impellers at off-design conditions. By comparing with the traditional inlet guide vanes CF (IGVCF), the application of the FAA improves the energy efficiency of the CF system significantly. A wider high-efficiency range is obtained, which is superior to the IGVCF. The numerical results effectively reveal the mechanisms by which flow loss occurs in the FAACF at the outlet of blades. In particular, the energy-saving rate of the G4-73-11 type CF system based on the FAA can reach 20.4 % when the relative flow ratio decreases to 74 %, which proves the positive adjustment potential of energy efficiency and indicates great energy-saving benefits of the FAA application in the CF system.

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.

Medium- and Long-term Forecast of China’s Electricity Consumption Considering the Fast Growth of New Infrastructure

In order to predict China’s electricity consumption demand under situation of fast growth of the new infrastructure, a medium- and long-term electricity consumption demand prediction model based on LEAP is proposed, which calculates the overall electricity consumption of the country according to the activity level and intensity of typical new infrastructure and other sectors. Five different scenarios are set for comparison. The results show that under the basic scenario, China’s electricity consumption will reach 7408 billion kWh in 2035, in which the consumption of new infrastructure accounts for 31.31%.While under the other four scenarios, China’s electricity consumption will reach to the minimum of 7319 billion kWh under the scenario of improvement of energy-saving technology, and the maximum of 7525 billion kWh under the scenario of fast development of the new infrastructure, with higher contributions of the consumption of commercial charging, data centers and 5G base station. Relevant suggestions are put forward from the aspects of new infrastructure construction, development of clean energy and technical standards to reduce the electricity consumption of new infrastructure.

Research on the optimization strategy of customers’ electricity consumption based on big data

Current power systems face significant challenges in supporting large-scale access to new energy sources, and the potential of existing flexible resources needs to be fully explored from the power supply, grid, and customer perspectives. This paper proposes a multi-objective electricity consumption optimization strategy considering the correlation between equipment and electricity consumption. It constructs a multi-objective electricity consumption optimization model that considers the correlation between equipment and electricity consumption to maximize economy and comfort. The results show that the proposed method can accurately assess the potential for electricity consumption optimization and obtain an optimal multi-objective electricity consumption strategy based on customers’ actual electricity consumption demand.

Forecasting Peak Electricity Consumption Demand in Luzon by utilizing ARIMA Model

This research study focuses on forecasting the future values of peak demand in electricity consumption for Luzon, Philippines based on monthly historical data spanning from 2001 to 2020. The data was obtained from the official website of the Philippines Department of Energy (DOE). The primary objective of this study is to employ the ARIMA (Autoregressive Integrated Moving Average) model-building procedure developed by Box and Jenkins to accomplish accurate peak demand forecasting. The methodology involved conducting various tests and evaluations to identify the ARIMA model with the least Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). After careful analysis, the best-fitting ARIMA model was determined to be ARIMA (11, 1, 12). The findings of this study indicate that, according to the ARIMA (11, 1, 12) model, Luzon's peak demand is projected to reach 10,497.65 megawatts by December 2021. Furthermore, the model predicts that by the end of 2022, 2023, and 2024, Luzon's peak demand will be approximately 10,738.34 MW, 10,953.98 MW, and 11,148.43 MW per electrical grid, respectively. The accuracy of the ARIMA (11, 1, 12) model is found to be satisfactory, with a low MAPE value of 3.639% and the most negligible RMSE value of 517.132. The implications of these forecasted peak demand values are significant for decision-makers in the energy and utilities sector. The accurate predictions provided by the ARIMA model can aid in resource allocation, infrastructure planning, and overall operational strategies to effectively meet the anticipated high-demand periods. In conclusion, this study successfully forecasts Luzon's future values of peak demand in electricity consumption using the ARIMA (11, 1, 12) model. The findings highlight the importance of accurate peak demand forecasting and provide valuable insights for energy industry professionals.

RESEARCH OF GLOBAL ENERGY TRENDS AIMED AT THE DEVELOPMENT OF ACTIVE ENERGY COMPLEXES

The article is devoted to the study of modern trends in the development of the global fuel and energy complex with the identification of features and key areas that determine the nearest policy in the field of energy conservation and energy efficiency improvement. The materials analyze the dynamics of changes in demand for consumption of various types of energy resources, changes in the structure of the world energy balance, emphasizing the continuing low level of energy efficiency in many countries of the world. Taking into account the significant amount of investments in renewable energy development technologies, the high growth rates of installed capacity of power plants operating on renewable energy sources over the past decade, as well as the high rates of global growth in demand for electricity consumption, one of the key areas in improving the efficiency of the global fuel and energy complex will be the use of distributed generation, including among those operating on the basis of renewable energy sources. Integration of technologies of small distributed generation with modern technologies of energy consumption management at the level of final consumption, such as management, demand for electricity and natural gas consumption and the formation of active energy complexes on the basis of industrial enterprises, will be full-fledged participants in the circulation of energy resources in Unified energy systems and Unified gas supply systems of the future.

The effect of extreme temperature on electricity consumption, air pollution, and gross domestic product

The Report: Intergovernmental Panel on Climate Change (IPCC) set a goal global warming of 1.5°C on global temperature change. Extreme climate changes have increased the demand for electricity consumption by people and enterprises. In fact, China's total power generation in 2019 exceeded 25% of the world's amount, and its thermal power generation accounted for more than 70%. Although past research on electricity efficiency seldom discusses the issue of climate change, the topic still remains important. This research thus considers extreme temperature days (climate change variable) as exogenous variable and uses the Two-Stage Meta Under exogenous undesirable EBM model to examine power efficiency in China. The results are as follows. (1) In the west only Qinghai's GDP, CO2, PM2.5, and electricity consumption have technology gap ratio (TGR) efficiency values of 1 in the 5 years. (2) China's electricity consumption has the same trend with the TGR efficiency of CO2 and is higher than PM2.5. (3) The national overall efficiency, meta overall efficiency, and TGR overall efficiency are the worst in China's west region.

A Time-Varying Incentive Optimization for Interactive Demand Response Based on Two-Step Clustering

With the increasing marketization of electricity, residential users are gradually participating in various businesses of power utility companies, and there are more and more interactive adjustments between load, source, and grid. However, the participation of large-scale users has also brought challenges to the grid companies in carrying out demand-side dispatching work. The user load response is uneven, and users’ behavioral characteristics are highly differentiated. It is necessary to consider the differences in users’ electricity consumption demand in the design of the peak–valley load time-sharing incentives, and to adopt a more flexible incentive form. In this context, this paper first establishes a comprehensive clustering method integrating k-means and self-organizing networks (SONs) for the two-step clustering and a BP neural network for reverse adjustment and correction. Then, a time-varying incentive optimization for interactive demand response based on two-step clustering is introduced. Furthermore, based on the different clustering results of customers, the peak–valley load time-sharing incentives are formulated. The proposed method is validated through case studies, where the results indicate that our method can effectively improve the users’ load characteristics and reduce the users’ electricity costs compared to the existing methods.

Prospects for Digitalization of Energy and Agro-Industrial Complex of Kazakhstan

The goal is to conduct modeling and analyze the management efficiency of the energy industry and the agro-industrial complex based on the introduction of digitalization to stimulate economic growth in Kazakhstan. When writing the work, the following research methods were used - bibliometric, statistical, factorial, grouping and generalization, regression, comparative. Results - an economic assessment of the development of the electric power industry and the agro-industrial complex was carried out. It is noted that the economy of Kazakhstan is quite energy-intensive and costly. It is shown that the increasing level of agricultural production exceeds the demand for electricity consumption. The serious deterioration of electrical equipment and agricultural machinery has been identified, which is a barrier to the promotion of digital technologies. The stimulation of the growth of electricity generation from renewable energy sources based on the use of innovative and digital technologies is analyzed. The modeling of the development of the agro-industrial complex is carried out on the basis of the use of information, communication and digital technologies of smart farms on the example of the Almaty region. Presented and summarized measures for the implementation of innovative projects in digital energy and smart agriculture. The problems and recommendations are given for accelerating the implementation of digitalization in the energy sector and the agro-industrial complex. The role of digital and electronic technologies in agriculture and the energy complex in promoting the economic growth of Kazakhstan has been substantiated.

Statistical Analysis of Trends in Electricity Consumption with Reference to Uttarakhand

Electricity consumption indicates countries growth and development. The aim of the paper is to study the trend of per capita consumption of electricity in Uttarakhand during the years 2011 to 2019. The regression techniques with linear regression, quadratic regression and exponential regression were used to carry analysis and to examine trends between number of years and per capita consumption of electricity in kwh (kilowatt hours). The present study suggests the best fit model by comparing R square, adjusted R square and residual means square error (RMSE). The finding suggests that the quadratic regression model is the best fit model for per capita consumption of electricity with R square (coefficient of determination) of 0.95 for forecasting of electricity consumption per capita in Uttarakhand. This will support policy makers and related sectors in order to meet the growing demand of electricity consumption of Uttarakhand.

New Conceptual Design of an Integrated Allam-Cycle Power Complex Coupling Air Separation Unit and Ammonia Plant

To meet the increasing demand for electricity consumption for global economic development and the urgent need for carbon dioxide (CO2) sequestration for global climate change mitigation, the natural-gas-fired Allam-cycle power plant becomes very attractive due to its high energy efficiency, NOx emission-free properties, and inherent capability of producing pipeline-ready CO2 for various industrial uses. Bearing these merits, this paper has newly developed an integrated A3 complex, which integrates an Allam power cycle, an air separation unit (ASU), and an ammonia plant to simultaneously produce power, ammonia, water, and pipeline-ready CO2 efficiently and economically. The developed A3 complex is highly material-and-energy integrated among the three subsystems, where the Allam power cycle will empower the ASU and in turn be fueled by the pure oxygen feed from the ASU; meanwhile, the nitrogen stream from the ASU will be the feedstock of the ammonia plant to produce fertilizers. The efficacy of the developed A3 complex has been demonstrated by rigorous large-scale modeling and simulations. Its economic and environmental performances have also been systematically evaluated to exemplify its system excellence.

The influence of primary measured parameters and electricity consumption in automatic solid fuels-fired boilers on the seasonal space heating energy efficiency

Commission Regulation (EU) 2015/1189 of 28 April 2015 implementing Directive 2009/125/EC of the European Parliament and of the Council with regard to Ecodesign requirements for solid fuel boilers presents the way to determine the seasonal space heating energy efficiency (ηs). These requirements are important to develop environmental-friendly sources for space heating. The value of ηs depends on (inter alia) a negative contribution to the ηs by auxiliary electricity consumption. In the current work, the analysis of sensitivity of electricity consumption measurement on ηs has been presented. The analysis includes also other specific measured parameters like water flow rate, water temperature, fuel consumption and higher heating value. Moreover, the impact of specific heat losses on the efficiency of the boiler is discussed and analyzed. The experimental and analytical investigations of 69 boilers (52 coal-fired and 17 biomass-fired) were carried out in the power range of 12–517 kW (nominal) and thermal efficiency of 83.1–96.3%. It was proved that the available relative uncertainty of electricity consumption gauge (i.e. <5%, guaranteed by suppliers) is acceptable to obtain accurate analysis. The seasonal space heating energy efficiency in automatic solid fuels-fired boilers was experimentally determined for 69 units, and the electricity consumption demand was less than 1% of nominal thermal power. The uncertainty of water temperature measurement has the strongest influence on the ηs uncertainty. The absolute uncertainty of temperature measurement at level of 5 °C caused the increase of ηs relative uncertainty to the level higher than 100%. The presented analysis has significant meaning from the practice of the ηs determination and the strategy of uncertainty budget, as well as measurement equipment calibration.

AGRICULTURAL GROWTH AND SUBSIDIZED POWER SUPPLY – FARMER CONSUMER'S PERSPECTIVE

There is a growing demand of electrical energy for agricultural usage requirements in India.Electrical Utilities of many states have been facing acute power shortage which led to unrest in the farmers in many states.It is observed in the last few decades,the underground water levels have been falling down drastically and cultivated area has been increasing by cutting the forests. Hence, there is growing demand for electricity consumption for the purpose of irrigation. The generation is not growing proportionately to the growing demand. On the other side, the available energy is also not properly utilized for Agricultural purposes. The farmers have to be educated in the area of energy conservation and effective utilization of available resources in the country. This paper highlights the role of power in agricultural development and throws light on review of literature with regard to farmers’ perception on free farm electricity and groundwater extraction.

DETERMINANTS OF HOUSEHOLD ELECTRICITY DEMAND IN RURAL INDIA: A CASE STUDY OF THE IMPACTS OF GOVERNMENT SUBSIDIES AND SURCHARGES

This paper analyses the determinants of household electricity consumption with the focus to find the impact of government subsidies and surcharges on the demand for electricity services in the rural areas. Using surveyed household data of 332 samples, quantile regression has been employed for checking heterogeneity in electricity demand across different quantile of households. We find government subsidy has enhanced the household demand for electricity consumption with the elasticity ranging from 45 to 65 percent. Skeptically, electricity consumers of higher quantile tend to consume more even in the presence of outstanding bill while it is the opposite for low quantile group. Surprisingly, income and other socioeconomics variables don't necessarily affect the households demand for electricity. This implies demand for electricity is inelastic to income and selected socioeconomic variables in rural regions. However, electricity demand decreases for households with dwelling characteristics categorized as poorer quantile. Based on our empirical findings implications are drawn for policy makers.Keywords: Electricity; Determinants; Heterogeneity; Quantile; Subsidies; Surcharges JEL Classifications: Q4; Q48; R2; R48DOI: https://doi.org/10.32479/ijeep.11716

Biogas to Fuel Cell State of The Art: A Review

Due to the emerging development in the energy industry, the demand for electricity consumption has sharply increased for each country. Therefore, a new recovery of energy resources is needed in consequence of the decreasing dependency on conventional energy resources, while sustaining energy security in the aspect of energy supply and climate change issues. The fuel cell is one of the most potential resources to be explored in order to overcome the constraints of the current energy generation. The aim of this paper is to discuss the entire cycle of the fuel cell system. It is starting from biogas production up to the recent studies related to life cycle assessment on fuel cell studies. Most of the researchers focused on the technical part of fuel cells; however, a comprehensive environmental assessment is essential to fully recognize the impact of fuel cells. Furthermore, this conceptual paper provided an idea on understanding the concept of fuel cell and referred to recently published articles related to life cycle assessment. Hopefully, this study can provide the guideline in determining the future energy for this country, in order to be less dependent on the current resources of energy supply.

Developing solar energy in household scale at Tan Binh District, Ho Chi Minh City, Vietnam

In 2019, Ho Chi Minh City has a total population of 8.99 million with GRDP around 6.021 USD/year [1]. Given the population size and the above GDP level, the electricity consumption demand for production and living purposes in the City was 83 million kWh per day in 2019 [2]. With high electricity demand, it must have developed the rate of renewable energy supply. And in Tan Binh district area, the population is considered quite high and is one of the economic development areas of the city. In Ho Chi Minh City, the sufficient supply of electricity is the top concern. And Tan Binh district is one of 24 districts belong to Ho Chi Minh City with crowded residential and industrial parks, that also has 1-2 houses applying solar energy. In there, solar energy is developing with industrial scale in many enterprises. So, this research was initial survey the potential using solar energy for household and the challenges of resident in Tan Binh district about applying solar energy for their home. According this study, now there is 1 house using and rating positively about solar energy combine national power grid. Around 92% people (1,500 interviewers in 15 wards) think that using solar energy is potential applying in their home because it gains the economic efficiency for reducing electricity fee. But it’s only 23% household interested in using solar energy in their house, when 50% citizens unsure about that. As the results, the factors effective their decision are the policies support from national government about using solar energy and the increasing of electricity bill. It’s a point for encounrage the applying solar energy in household scale in Ho Chi Minh.

A Two-Stage Multiple Criteria Decision Making for Site Selection of Solar Photovoltaic (PV) Power Plant: A Case Study in Taiwan

At the heart of Covid-19 responses, the transition from fossil sources to green energy is an urgent issue for nations to address the crisis and secure sustainable economies. As a country in a seismically active zone that relies heavily on imported fossil fuels, Taiwan is vigorously taking the next step in renewable energy development, which is pivotal to securing its position in global supply chains. Solar energy is today the most suitable renewable energy source for Taiwan. However, land prices and policies, and challenges of scale still hinder its development. In this context, identifying optimal sites for solar photovoltaic (PV) construction is a crucial task for major energy stakeholders. In this paper, a two-stage approach, combining the data envelopment analysis (DEA) models and the analytic hierarchy process (AHP), has been done for the first time to identify the most suitable locations among 20 potential cities and counties of Taiwan for constructing solar PV farms. DEA models were applied to filter out the areas with the most potential by measuring their efficiency indices with temperature, wind speed, humidity, precipitation, and air pressure, as inputs, and sunshine hours and insolation, as outputs. The locations with perfect efficiency scores were then ranked with the AHP method. Five selected evaluation criteria (site characteristics, technical, economic, social, and environmental) and sub-criteria of each were utilized to prioritize the locations with solar energy potential. AHP was used to determine the relative weights of the criteria and sub-criteria and the final weights of the areas. For criteria weighting results, “support mechanisms,” “electric power transmission cost,” and “electricity consumption demand” with weights of 0.332, 0.122, and 0.086, respectively, were found as the most significant sub-criteria. The final ranking suggests Tainan, Changhua, and Kaohsiung as the top three most suitable cities for constructing solar PV energy systems.

Prediction of water-light output in water-light complementary systems

With the increasing demand of electricity consumption for social development, China has vigorously promoted the development of renewable energy such as wind and solar energy in recent years. Since photovoltaic power generation has the characteristics of randomness, intermittency and volatility, which are not conducive to dispatching and grid-connection, and hydroelectric power generation has the characteristics of fast start-up and good peak adjustment, etc. Therefore, through the study of multiple linear regression model neural network prediction model Markov chain and other methods, the hydraulic resource output prediction model and photovoltaic resource output prediction model are built. The combination of the two can be used to predict the output of water and light resources more accurately and promote the development of water and light complementary industry in China. At present, there are few researches on the coupling of hydroelectric output and photovoltaic output at home and abroad. In this paper, the prediction of water-light output in water-light complementary systems by other scholars is reviewed.

Electricity demand management within the retail electricity market in Russia

Demand side management is an important tool for ensuring the flexibility of the electric power systems, as it maintains and regulates the balance of generation and consumption of electric energy and has a system-wide effect, which is formed by lowering electricity prices for consumers and optimizing the load and structure of generating and electric grid capacities. The article is devoted to the problem of managing the demand for electricity consumption within the framework of the retail electricity market in Russia. The author identifies and summarizes the existing demand management tools. The article substantiates the need for the development and implementation of innovative tools and mechanisms for managing the demand for electricity consumption.