Industrial Electricity Consumption Research Articles

Prediction of industrial electricity consumption based on grey cluster weighted Markov model

Accurate prediction of industrial electricity consumption is not only beneficial to maintaining the steady development of the economy but also to conserving energy. To improve the prediction accuracy of industrial electricity consumption, a grey cluster weighted Markov model is proposed. It is applied to predict the industrial electricity consumption in four different regions in China. The prediction results are compared with the traditional discrete grey prediction model, which shows that the present model is more effective in these aspects of prediction accuracy, stability and extensibility. The research can provide theoretical references for the “West-East electricity transmission project” in China.

A novel time-varying grey Fourier model for variable amplitude seasonal fluctuation sequences

PurposeA time-varying grey Fourier model (TVGFM(1,1,N)) is proposed for the simulation of variable amplitude seasonal fluctuation time series, as the performance of traditional grey models can't catch the time-varying trend well.Design/methodology/approachThe proposed model couples Fourier series and linear time-varying terms as the grey action, to describe the characteristics of variable amplitude and seasonality. The truncated Fourier order N is preselected from the alternative order set by Nyquist-Shannon sampling theorem and the principle of simplicity, then the optimal Fourier order is determined by hold-out method to improve the robustness of the proposed model. Initial value correction and the multiple transformation are also studied to improve the precision.FindingsThe new model has a broader applicability range as a result of the new grey action, attaining higher fitting and forecasting accuracy. The numerical experiment of a generated monthly time series indicates the proposed model can accurately fit the variable amplitude seasonal sequence, in which the mean absolute percentage error (MAPE) is only 0.01%, and the complex simulations based on Monte-Carlo method testify the validity of the proposed model. The results of monthly electricity consumption in China's primary industry, demonstrate the proposed model catches the time-varying trend and has good performances, where MAPEF and MAPET are below 5%. Moreover, the proposed TVGFM(1,1,N) model is superior to the benchmark models, grey polynomial model (GMP(1,1,N)), grey Fourier model (GFM(1,1,N)), seasonal grey model (SGM(1,1)), seasonal ARIMA model seasonal autoregressive integrated moving average model (SARIMA) and support vector regression (SVR).Originality/valueThe parameter estimates and forecasting of the new proposed TVGFM are studied, and the good fitting and forecasting accuracy of time-varying amplitude seasonal fluctuation series are testified by numerical simulations and a case study.

A Novel Rolling and Fractional-ordered Grey System Model and Its Application for Predicting Industrial Electricity Consumption

A Novel Rolling and Fractional-ordered Grey System Model and Its Application for Predicting Industrial Electricity Consumption

Analysis of Long-Term and Short-Term Relationships between Electricity Consumption and Economic Growth in Industrialized Regions of Russia

The purpose of the proposed study is to identify long-term and short-term cause-and-effect relationships between industrial electricity consumption and economic growth through comparative analysis of two neighboring regions with approximately the same industrial potential - the Sverdlovsk and Chelyabinsk regions. To solve this problem, an econometric approach is used, based on the method of testing the boundaries of autoregressive and distributed lag (ARDL) models, which determines the presence of cointegration between series. The use of this method is indispensable when studying regional problems due to the insufficient length of time series of economic indicators in the region. The variables in the comparative analysis were industrial electricity consumption, industrial production volume, economic growth rate, per capita income, and average annual number of employees. When analyzing the data, it was revealed that significant cointegrated variables for the Sverdlovsk region are the rate of economic growth and electricity consumption. Accordingly, for the Chelyabinsk region these variables are the volume of industrial production, electricity consumption and the average annual number of employees. That is, the electricity consumption of the Sverdlovsk region in the long term does not depend on the volume of industrial production and the number of employees but depends only on the rate of economic growth. In the Chelyabinsk region, accordingly, in the long term, electricity consumption depends on the volume of industrial production, the number of employees and does not depend on growth rates. Thus, the regions that, at first glance, are similar in industrial potential differ in the cause-and-effect relationships between economic growth and industrial electricity consumption. The use of causality tests made it possible to identify long-term and short-term cause-and-effect relationships between variables. The results obtained in this study illustrate the explanatory and predictive capabilities of the econometric approach in the context of analyzing cause-and-effect relationships in the economy of two neighboring regions and its energy system. These results may be important when analyzing electricity consumption and energy saving in the industrial sector of the economy of these areas.

Interprovincial Transfer Entropy Analysis on Electricity Consumption by Secondary and Tertiary Industries

As electricity consumption is direct feedback of the production and operation state of an industry, there should be links between lead-lag relation in electricity consumption volumes and driving/transferring effects cross regions and industries. In this paper, lead-lag relation between provincial secondary industry (SI) and tertiary industry (TI) electricity consumption volumes is studied based on transfer entropy (TE) theory. Significant TE flow from SI to TI is observed both intra-provincially and interprovincially in a large part of China and periodic fluctuations in electricity consumption curves of SI emerge stably one month ahead of that of TI. It is also found that the curves of SI electricity consumption show more significant correlation with economic cycle in GDP than that of TI. Meanwhile, in both SI and TI cases, Shanghai (SH) manifests noticeable driving effect on other provinces, especially those in Yangtze River Economic Belt. Maximum spanning arborescence topology is built to exhibit direct affecting routes with maximized TE flows of SI and TI from SH to other provinces in the Belt. It is deduced that in SI case it takes stages for the influence from SH to spread while in TI case the influence tends to directly radiate elsewhere. The study attempts to propose an approach to looking through the economy from a perspective of electricity consumption and provide a reference for making industrial strategy.

COVID-19 karantinasının elektrik tüketimine ve üretimine etkisi: Türkiye örneği

In order to reduce the negative impact of the coronavirus (COVID-19), which first emerged in Wuhan, China measures such as distance education and curfews were taken by governments to reduce the negative impact on human health. These precautions had an impact on electricity consumption. Turkey was one of the countries adversely affected by COVID-19. Therefore, the objective of this paper analyze the variation of electricity consumption and generation in Turkey depending on the COVID-19 outbreake. For this purpose, residential, industrial, business and total electricity consumption of six provinces was analyzed in order to examine the variation in electricity consumption in Turkey. Considering the ten months of the year, with March when the first case occurred in 2020 compared to 2019, it was observed that residential electricity consumption increased in the six provinces studied. Industrial electricity consumption decreased in March and the following months, but increased in the following months. On the other hand, it was observed that there was a decrease in the business electricity consumption in almost all ten months. In the per capita electricity generation estimation study in Turkey, it was calculated that the electricity generated in 2020 is 5.89% more than the estimated value obtained by the curve fitting method. In addition, according to the study of coverage the electricity consumption and generation per capita for the years 2019 and 2020, it was calculated that generation could not coverage the consumption by 0.22% in 2019 and 0.27% in 2020.

A novel hybrid grey system forecasting model based on seasonal fluctuation characteristics for electricity consumption in primary industry

As a critical economic index, the annual electricity consumption plays an essential role in reflecting the domestic economic environment. For the sustainable development of the electricity power industry, an accurate electricity consumption forecasting model can not only guide the power generation work in advance but also help to determine the regulation law of electricity economic development. To accurately describe the seasonal fluctuation characteristics of electricity consumption in China's primary industry, Fourier modified grey forecasting model based on seasonal fluctuation characteristics and initial condition optimization (FDSGM(1,1,x(β),γ)) was put forward. Based on the empirical data of seasonal electricity consumption in China's primary industry, comparing with SGM(1,1), RSGM(1,1) and DSGM(1,1), the minimum mean absolute percentage errors (MAPEs) in the training set was reduced to 0.2 %, while that in the testing set was also reduced to 6.02 %, which was significantly better than those of the previous three forecasting models and was also markedly better than the prediction accuracy of reference [15]. The results indicated that FDSGM(1,1,x(β),γ) could identify seasonal fluctuations and variations and was a reliable seasonal forecasting tool that could be used to ensure sustainable development of electricity markets.

Influence of PPD and Mass Scaling Parameter on the Goodness of Fit of Dry Ice Compaction Curve Obtained in Numerical Simulations Utilizing Smoothed Particle Method (SPH) for Improving the Energy Efficiency of Dry Ice Compaction Process

The urgent need to reduce industrial electricity consumption due to diminishing fossil fuels and environmental concerns drives the pursuit of energy-efficient production processes. This study addresses this challenge by investigating the Smoothed Particle Method (SPH) for simulating dry ice compaction, an intricate process poorly addressed by conventional methods. The Finite Element Method (FEM) and SPH have been dealt with by researchers, yet a gap persists regarding SPH mesh parameters’ influence on the empirical curve fit. This research systematically explores Particle Packing Density (PPD) and Mass Scaling (MS) effects on the agreement between simulation and experimental outputs. The Sum of Squared Errors (SSE) method was used for this assessment. By comparing the obtained FEM and SPH results under diverse PPD and MS settings, this study sheds light on the SPH method’s potential in optimizing the dry ice compaction process’s efficiency. The SSE based analyses showed that the goodness of fit did not vary considerably for PDD values of 4 and up. In the case of MS, a better fit was obtained for its lower values. In turn, for the ultimate compression force FC, an empirical curve fit was obtained for PDD values of 4 and up. That said, the value of MS had no significant bearing on the ultimate compression force FC. The insights gleaned from this research can largely improve the existing sustainability practices and process design in various energy-conscious industries.

Modeling and forecasting electricity consumption in Nigeria using Arima and Arimax time series models

This study compared the extrapolation strengths of two models: Autoregressive Integrated Moving Average (ARIMA) and Autoregressive Integrated Moving Average with an Exogenous Variable (ARIMAX) in the forecast of Nigeria's electricity consumption. Annual data on power generation and consumption from the Central Bank of Nigeria statistical bulletin for 2006 and 2016 over a 51-year period (1970-2020) was used. Industrial and residential electricity consumptions were examined for possible unit roots (non-stationarity) using the Augmented Dickey-Fuller test approach. The ADF test result showed that the time series achieved a stationary state for the variables under consideration at first difference. Akaike Information Criterion (AIC) and Root Mean Square Error (RMSE) were used to assess the performance of each models. Comparing the ARIMA and ARIMAX forecast models, ARIMA(0, 1, 1) emerged for modelling and forecasting industrial electricity consumption in Nigeria while ARIMAX (1, 1, 1) with installation capacity as exogenous variable was suitable for modelling and forecasting residential electricity consumption in Nigeria. The study recommended that for optimal residential electricity consumption in Nigeria, installation capacity and the total power generation in Nigeria should be enhanced.

Modeling and forecasting industrial electricity demand for Saudi Arabia: Uncovering regional characteristics

This study aims to investigate and forecast Saudi Arabia's industrial electricity consumption at the regional level. By applying structural time series modeling to annual data from 1990 to 2019, we find the electricity demand's long-run income and price elasticities to vary across regions. Also, the underlying energy demand trend analysis indicates efficiency improvements in industrial electricity consumption patterns in all regions. In the low-case scenario, we project the 2030 demand to be 11.6 terawatt-hours (TWh), 63.5 TWh, 0.7 TWh, and 6.8 TWh for the central, eastern, southern, and western operation areas, respectively, with the total demand forecast to be 82.5 TWh. The study's findings can help policymakers understand the effect of future energy policies and economic growth trajectories.

The potential of load identification and regulation in the textile industry

Firstly, this paper introduces the daily load characteristics and annual load characteristics of the textile industry and points out that the electricity load of the textile industry shows obvious seasonal changes. There is a certain degree of randomness. Then, this paper analyzes the behavior characteristics of electricity consumption in the textile industry, including the behavior mode of electricity consumption and the characteristics of electric equipment. It points out that the textile industry has a certain potential and space in energy utilization. Finally, this paper focuses on the load regulation situation of the textile industry and summarizes the load proportion, regulation mode, regulation time, and adjustable load proportion of the textile industry. This paper studies and discusses the energy utilization and load control of the textile industry, which has certain theoretical and practical significance.

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.

Forecasting models for the electricity consumption of the cement industry in Brazil

The consumption of electric energy in the Brazilian industrial sector has been investigated over the past few years. This interest is related to sector development, energy planning, and energy efficiency. Thus, forecasting models are important for decision-making. The objective of this work is to compare different forecasting models applied to monthly data on electric energy consumption in the cement industry in Brazil. Therefore, the Holt-Winters method, the Seasonal ARIMA model, the dynamic linear model, and the autoregressive neural network model were used. Through the considered accuracy metrics, the Seasonal ARIMA model showed the best predictive performance for the analyzed period.

Evaluation System of Light Pollution Risk Index Based on Entropy Weight Method

In recent years, the issue of light pollution has garnered widespread attention. To address this concern, the state of California in the United States has developed a grading format to regulate lighting areas for light pollution protection. In order to further study this problem, we have selected six factors related to light pollution, namely population density, per capita gross regional product, total passenger transport, industrial electricity consumption, real estate development investment, and green area of built-up areas in different years in each prefecture-level city in China. Based on the VIKOR model, we have established a light pollution risk assessment system using the obtained compromise value as an assessment index for light pollution levels in different regions. The results have been analyzed using VIIRS satellite remote sensing images, and the average error is 5.24%, indicating good sensitivity to the system. Finally, we have analyzed the assessment results to show that over-development of areas can seriously exacerbate the risk level of light pollution. Therefore, development should be planned rationally to reduce the impact of light pollution.

Analysis of the Influencing Factors of Power Demand in Beijing Based on the LMDI Model

Since the reform and opening-up, under the new economic situation and policy, the rapid growth of power demand in Beijing is threatening the sustainable development of China’s economy and environment. To recognize the driving factors of electricity consumption growth and offer policy implications, based on the data of electricity consumption, the Gross Domestic Product (GDP) and the resident population in Beijing from 1990 to 2021, this research used the Kaya-equation and logarithmic mean divisia index (LMDI) model to decompose the growth of power demand in Beijing into the quantitative contribution of each driving factor from the perspective of industrial electricity consumption and residential electricity consumption. The results of the decomposition analysis show that, as far as industrial electricity consumption is concerned, the contribution rates of economic growth, electricity consumption intensity and output value structure to industrial electricity growth are 234.26%, −109.01% and −25.25%, respectively, which shows that economic growth is the primary driving force promoting the growth of industrial electricity demand. Power consumption intensity is the main reason for restraining the growth of industrial power demand, the growth rate is sliding and the contribution of the industrial structure is relatively small; as far as residential power consumption is concerned, the contribution rates of per capita power consumption and population size to residential power growth are 68.13% and 31.87%, respectively, which indicates that per capita power consumption is the main factor promoting the growth of residential power demand, followed by the total population. The study results show that the consumption of electric power would increase if Beijing’s economy and urbanization keep developing, and optimizing the industry structure, improving the efficiency of electric energy utilization and adopting clean power energy are the main approaches to making Beijing’s consumption of electric power decrease.

Research on Power Load Forecasting Using Deep Neural Network and Wavelet Transform

In today's rapid economic development, industrial and civil electricity consumption is growing year by year, and how to guarantee stability of power system operation has become the focus of attention of the power sector in each country. Power load forecasting has been closely associated with the modernization of power system management and is a vital guarantee for the safe and stable operation and economic efficiency of the power system. In this article, the authors propose a recurrent neural network (RNN) decision fusion forecasting framework based on the wavelet transform to address the power load forecasting problem. The framework firstly performs the wavelet transform on the power load data and uses Daubechies wavelets to extract the high-frequency and low-frequency parts of the data; then the data with different frequencies are combined with the original data and fed into the RNN model separately, and the decision fusion is performed in the output layer; finally, the prediction results are obtained by superposition of two RNN networks. The results showed that the error of the predicted data in the last nine years decreased by 50%, compared with the traditional method of feeding the data into the RNN model for training, which provides a new idea for future power load forecasting.

Information Value of Individual and Consolidated Financial Statements for Indicative Liquidity Assessment of Polish Energy Groups in 2018–2021

Electricity is currently one of the most popular sources of energy. Considering such widespread use of electric energy, we may ask, what is the economic cost of producing and supplying it? The climate crisis and the social pressure associated with it have triggered the necessity to make further investments in renewable and low-emission energy sources, while the COVID-19 pandemic has abruptly limited electricity consumption in industry. All these factors can have an impact on disruptions or loss in the liquidity of companies responsible for supplying electricity to end users. Guaranteeing cash flow for energy sector entities is a prerequisite for energy supply continuity. In this context, the selection and application of reliable sources of information are vital for the management of the financial liquidity of energy sector entities. The aim of this article is to prove the value of the financial information of individual (IFR) and consolidated financial statements (CFR) essential for the indicative liquidity assessment of Polish energy groups in 2018–2021. The hypothesis of this study is that individual and consolidated statements do not offer coincident analytical data due to the diversified role of their parent undertakings. We have applied indicative liquidity assessment analysis from a static and dynamic perspective to 2018–2021, on the basis of individual and consolidated financial statements. The results clearly show high dysfunction in the application of indicative liquidity assessment in the case of the individual financial statement of the parent company. This is mainly due to the role parent companies play in Polish energy sector groups, as they are mainly responsible for support processes.

Which Type of Energy Consumption is Effective on Economic Growth? Industry or Residential? The Case of Nuts-2 Regions in Turkiye

This study aims to determine the causal relationship between energy consumption and growth at the regional level, considering energy use's production and consumption purpose. In this direction, the relationship between industrial and residential electricity consumption and economic growth in Turkey has been tested for 26 regions and the period 2007-2020 by the Kónya Bootstrap Panel Causality Approach. According to the analysis results, while 11 areas support the growth hypothesis in terms of industrial electricity consumption, only one shows a bidirectional relationship; that is, it gives results by the feedback hypothesis. Five regions support the growth hypothesis regarding residential electricity consumption, and only two show a bidirectional relationship. In addition, four areas that do not offer a bidirectional relationship have findings that support the conservation hypothesis. According to the results, using energy as an input in final consumption or production may cause different regional effects on growth. Considering these relations, especially when choosing regional development and energy policies, will bring the right results specific to the regions. In addition, the absence of a causal relationship between electricity consumption and growth in many of the 26 areas examined indicates regional problems in the efficient use of resources.

An approach for analyzing urban carbon emissions using machine learning models

Carbon peaking and carbon neutrality goals have posed great challenges to transforming local economies into low-carbon economies. Hence, establishing an effective carbon management system is urgent. However, the development of the urban carbon management system is hampered by the immaturity of the carbon emission accounting system at the city level. To compensate for the insufficiency of the existing urban carbon emission accounting system and to find the city government in constructing a perfect carbon emission management system as soon as possible, this study used the data science method based on the statistical data of 285 cities in China from 2005 to 2017 to explore the influencing factors of urban carbon emissions, that is, using light gradient boosting machine and the accumulated local effects interpretable models to screen potential influencing factors of urban carbon emissions. Then, an index system for urban carbon management was evaluated and proposed, and a case analysis was conducted with urban industrial electricity consumption as a background. This method can be easily integrated with the existing urban management system, which could reduce the cost of building a carbon management system.

Spatio-Temporal Distribution Characteristics and Drivers of PM2.5 Pollution in Henan Province, Central China, before and during the COVID-19 Epidemic.

PM2.5 is the main cause of haze pollution, and studying its spatio-temporal distribution and driving factors can provide a scientific basis for prevention and control policies. Therefore, this study uses air quality monitoring information and socioeconomic data before and during the COVID-19 outbreak in 18 prefecture-level cities in Henan Province from 2017 to 2020, using spatial autocorrelation analysis, ArcGIS mapping, and the spatial autocorrelation analysis. ArcGIS mapping and the Durbin model were used to reveal the characteristics of PM2.5 pollution in Henan Province in terms of spatial and temporal distribution characteristics and analyze its causes. The results show that: (1) The annual average PM2.5 concentration in Henan Province fluctuates, but decreases from 2017 to 2020, and is higher in the north and lower in the south. (2) The PM2.5 concentrations in Henan Province in 2017-2020 are positively autocorrelated spatially, with an obvious spatial spillover effect. Areas characterized by a high concentration saw an increase between 2017 and 2019, and a decrease in 2020; values in low-concentration areas remained stable, and the spatial range showed a decreasing trend. (3) The coefficients of socio-economic factors that increased the PM2.5 concentration were construction output value > industrial electricity consumption > energy intensity; those with negative effects were: environmental regulation > green space coverage ratio > population density. Lastly, PM2.5 concentrations were negatively correlated with precipitation and temperature, and positively correlated with humidity. Traffic and production restrictions during the COVID-19 epidemic also improved air quality.