Industrial Electricity Consumption Research Articles

The Validity of Electricity Consumption as an Indicator for Economic Evaluation: Cross-Country Analysis

Abstract: This article aims to investigate the validity of using electricity consumption as an indicator to evaluate economic conditions and explore the relationship of this indicator across different countries. The study reveals a positive correlation between electricity consumption and the growth rate of Gross Domestic Product (GDP), which becomes more significant in the post-21st century period for middle-income, high-income countries, and globally. The relationship between the growth rate of industrial value-added and electricity consumption exhibits dynamic variations. It weakens for lower-middle-income countries but strengthens for upper-middle-income and high-income countries in the post-21st century. Moreover, there is a strong positive correlation between the logarithm of GDP and the logarithm of electricity consumption, particularly pronounced for high-income countries, with a significant increase in correlation in the 21st century. Similarly, a strong positive correlation exists between the logarithm of industrial value-added and the logarithm of electricity consumption, especially notable for high-income countries where the correlation further intensifies in the post-21st century. In conclusion, electricity consumption can serve as an effective indicator for evaluating economic conditions, with its relationship to economic growth and industrialization influenced by the income level of countries.

Optimal Dispatching Strategy for Textile-Based Virtual Power Plants Participating in GridLoad Interactions Driven by Energy Price

The electricity consumption of the textile industry accounts for 2.12% of the total electricity consumption in society, making it one of the high-energy-consuming industries in China. The textile industry requires the use of a large amount of industrial steam at various temperatures during production processes, making its dispatch and operation more complex compared to conventional electricity–heat integrated energy systems. As an important demand-side management platform connecting the grid with distributed resources, a virtual power plant can aggregate textile industry users through an operator, regulating their energy consumption behavior and enhancing demand-side management efficiency. To effectively address the challenges in load regulation for textile industry users, this paper proposes a coordinated optimization dispatching method for electricity–steam virtual-based power plants focused on textile industrial parks. On one hand, targeting the impact of different energy prices on the energy usage behavior of textile industry users, an optimization dispatching model is established where the upper level consists of virtual power plant operators setting energy prices, and the lower level involves multiple textile industry users adjusting their purchase and sale strategies and changing their own energy usage behaviors accordingly. On the other hand, taking into account the energy consumption characteristics of steam, it is possible to optimize the production and storage behaviors of textile industry users during off-peak electricity periods in the power market. Through this electricity–steam optimization dispatching model, the virtual power plant operator’s revenue is maximized while the operating costs for textile industry users are minimized. Case study analyses demonstrate that this strategy can effectively enhance the overall economic benefits of the virtual power plant.

Do policies make a difference? Assessing the impact of China's air pollution prevention and control action plan on carbon emissions

This study aims to balance the dual objectives of improving environmental quality and achieving carbon peaking and carbon neutrality goals. Utilizing panel data from 274 Chinese cities between 2006 and 2020, this paper employs the difference-in-differences method to examine the impact of the Air Pollution Prevention and Control Action Plan (APPCAP) on urban carbon emissions. Empirical results indicate that the impact of the APPCAP on regional carbon emissions follows an inverted U-shaped curve, exhibiting a dual impact path. Although the implementation of the APPCAP increased the annual carbon emissions of the city by an average of 1.637 million tons, it showed a trend of inhibiting the growth of carbon emissions, although it was not significant. Specifically, the APPCAP is most effective in reducing carbon emissions during the "source prevention" stage, primarily by increasing industrial electricity consumption and reducing coal production and consumption. However, a rebound effect occurs at the policy period's end, with compensatory increases in crude oil and electricity consumption, thereby elevating regional carbon emission levels. The APPCAP's effect on carbon emissions is more pronounced in areas with lower population density and higher environmental regulation intensity. It notably raises carbon emission levels in non-industrial base and non-resource-based cities compared to other cities. Furthermore, the APPCAP positively impacts regional carbon emission intensity and economic growth, significantly driving the secondary industry while minimally affecting the primary industry.

Predicting Industrial Electricity Consumption Using Industry–Geography Relationships: A Graph-Based Machine Learning Approach

Accurately predicting industrial electricity consumption is of paramount importance for optimizing energy management and operational efficiency. Traditional forecasting approaches face significant challenges in capturing the complex factors influencing industrial electricity consumption, often due to the inadequate representation of correlations, thus limiting their predictive capabilities. To overcome these limitations, we propose a novel graph-based forecasting model termed Industry–Geography Time Series Forecasting Model (IG-TFM). Our approach leverages historical electricity consumption data and geographical information relevant to similar industries to construct an industry–geography relationship graph. This graph serves as the foundation of a comprehensive network that encompasses all industries of interest, allowing us to identify sectors closely associated with the target industry. The structured graph data are then processed within a graph convolutional neural network framework, which effectively captures the impact of geographical location, industry similarities, and inter-industry relationships on electricity consumption patterns. Utilizing this enriched representation, we develop our IG-TFM for accurate time series forecasting of industrial electricity consumption. Experiments conducted on real-world data, including 31 industries across 9 cities in a southern province of China, demonstrate the significant advantages of our proposed method across key performance indicators such as the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). These findings underscore the importance and efficacy of employing complex networks to encode sequence-related information, thereby substantially improving prediction accuracy in industrial electricity consumption forecasting.

A study on long-term industrial electricity consumption and carbon emission forecasting methods based on TimesNet models

Abstract Aiming at the prediction problem of industrial electricity consumption and carbon emission, a TimesNet model is established to improve the accuracy of industrial electricity consumption and carbon emission prediction. Five significant influencing factors were screened out by grey correlation analysis; based on the energy consumption dataset of an iron and steel enterprise in 2023, the dataset was preprocessed and inputted into the TimesNet model, the prediction results were integrated and summarized, and the prediction results of the DLinear, LightTS, PatchTST, and iTransformer time series prediction models were compared and analyzed. The results show that the predicted values of the TimesNet model fit the actual values, and its mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are 0.31, 0.36, 0.6, and 1.12, respectively, which are better than those of the other models. It proves that the model can predict industrial electricity usage and release of carbon more accurately, providing a practical method for industrial electricity usage and release of carbon prediction.

A Hybrid Approach for Hierarchical Forecasting of Industrial Electricity Consumption in Brazil

The Brazilian industrial sector is the largest electricity consumer in the power system. Energy planning in this sector is important mainly due to its economic, social, and environmental impact. In this context, electricity consumption analysis and projections are highly relevant for the decision-making of the industrial sectorand organizations operating in the energy system. The electricity consumption data from the Brazilian industrial sector can be organized into a hierarchical structure composed of each geographic region (South, Southeast, Midwest, Northeast, and North) and their respective states. This work proposes a hybrid approach that incorporates the projections obtained by the exponential smoothing and Box–Jenkins models to obtain the hierarchical forecasting of electricity consumption in the Brazilian industrial sector. The proposed approach was compared with the bottom-up, top-down, and optimal combination approaches, which are widely used for time series hierarchical forecasting. The performance of the models was evaluated using the mean absolute percentage error (MAPE) and root mean squared error (RMSE) precision measures. The results indicate that the proposed hybrid approach can contribute to the projection and analysis of industrial sector electricity consumption in Brazil.

Analyzing Determinants of Spatial Patterns in Total and Industrial Electricity Consumption in Turkey

This research investigates the spatial correlation among per capita electricity consumption, per capita industrial electricity consumption, and economic growth by employing various regression models, including linear regression, geographically weighted regression, and multi-stage geographically weighted regression. The primary goal is to illustrate the presence of spatial effects in the connection between electricity consumption and economic growth. In this context, this study made for Turkey distinguishes itself from previous research by utilizing the multi-stage spatially weighted regression model to examine this relationship. The findings reveal that the multi-scale spatial regression model is the most effective in explaining the relation between economic growth at the provincial level and per capita electricity consumption and per capita industrial electricity consumption. Moreover, the study emphasizes that per capita Gross Domestic Product emerges as the most influential regional economic indicator when assessing its impact on per capita electricity consumption and per capita industrial electricity consumption.

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

A Case Study of Determining Energy Efficiency in Squirrel Cage Induction Motor According to IEC 60034-2-1:2014 Standard

Efficiency and sustainability are the most important and focused issues today. Industrial systems are the main working field to reduce energy consumption because of its share in the total amount of energy. According to TSI (Turkey Statistical Institute) data, industry sector in Turkey consumed 47.1% of total electrical energy in 2013. Electric motor-based systems are the important type of electrical loads in the industry. In the European Union, these systems are estimated to account for about 70% of all industrial electricity consumption. Since 1998, the motor producers and the standard organizations are trying to classify electrical motors by efficiency with standard methods. Nowadays, in EU and the other relevant countries, the IEC standard methods are used to determine the efficiency class of the induction motors. The latest standard was published in December 2014 with the code IEC 60034-2-1. In this study, an IE3 class 22 kW 4 poles induction motor was tested and the efficiency value was calculated by using IEC 60034-2-1 standard method. And the results were evaluated according to eco-design requirements for electric motors (EU 640/2009). The study covers all tests and calculation steps clearly in this paper.

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.

Empirical analysis of monthly electricity consumption prediction in manufacturing industry using machine learning techniques

Abstract Electricity consumption prediction is an important part of power planning and the basis of power dispatch planning. The SVM and GBDT algorithms that were optimized by the PSO algorithm are used to build the machine learning-based electricity consumption prediction model in this paper. The decomposed and reconstructed high-frequency signals and low-frequency signals are optimized by the particle swarm algorithms SVM and GBDT for power consumption prediction, respectively. Improve the clustering performance of the traditional fuzzy C-mean algorithm for unbalanced data. The performance of the algorithms is analyzed in two different application scenarios, namely, artificial dataset and power users' real measurement dataset. Power consumption prediction in the manufacturing industry is performed. It is found that the ARI, FMI, and AMI index values of the improved algorithms proposed in this paper are 0.9543, 0.9347, and 0.9344, respectively, on the grid user-measured dataset, while the traditional DPC and K-means clustering algorithms are less effective. The machine learning algorithm optimized after wavelet decomposition increased R² by 8.98%, MAPE decreased by 19.78%, and RMSE decreased by 11.53% compared to PSO-GBDT, and the predictive evaluation indexes were all improved, and the two machine learning algorithms in this paper optimized by wavelet decomposition combined with PSO have good predictive effect R² increased from 0.801 to 0.842, and the two machine learning algorithms designed in this paper based on wavelet decomposition have good predictive effect R² increased from 0.801 to 0.842. The designed machine learning model for electricity consumption prediction based on wavelet decomposition and PSO has excellent performance, and the design expectation has been fulfilled. This paper makes a useful exploration and proposes an effective method for accurate prediction of electricity consumption in the manufacturing industry.

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.

Multi-source data-driven technology research on carbon emission dynamics prediction in electric power industry

Abstract This paper analyzes the trend of power generation structure and carbon emission changes in the power industry and decomposes and analyzes the influencing factors of carbon emission in the power industry by using the LMDI decomposition method. Combined with the analysis of the influencing factors of carbon emissions in the power industry from 2016 to 2022, the carbon emissions of the power industry in the Yellow River Basin are simulated by the scenario analysis method. Four simulation scenarios were constructed based on the economic scale, industrial structure, industrial electricity consumption intensity, thermal power fuel conversion rate, and power supply structure. The IPSO-LSTM model for carbon emission prediction was created after optimizing the LSTM neural network prediction model. Combining the scenario analysis method to set the amount of changes in the high carbon, baseline, and low carbon scenarios of the influencing factors, the carbon emissions from the power sector in different scenarios are predicted for the years 2025-2035. From 2025 to 2035, the carbon emissions from the power sector in the three scenarios, except for the energy transition scenario, show a trend of increasing, then decreasing, and then increasing over the study period. The energy transition scenario shows a pattern of increasing and decreasing carbon emissions from the power sector.

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.