Industrial Electricity Consumption Research Articles

Impact of energetics development on the sustainability of the regional economy

Gross regional product (GRP) per capita is the main indicator of the development level in a region, determining budgetary economic security and living standards of the population in the region. The energy sector has a great influence on the economic and sustainable development of the regions. The studying problem of the influence level in electricity consumption, the electrical equipment of labour, the volume of industry shipped products on the level of GRP is relevant. The main indicator of ensuring economic security in the region is a high level of GRP per capita. The factors affecting this indicator are revealed. The paper considers the degree of GRP dependence on the following indicators: electricity consumption in industry, the volume of goods shipped by manufacturing industries, the electrical equipment in industrial work (manufacturing industries). By the method of factor analysis, generalized characteristics of regional development are made.

Forecast for electricity consumption in the copper mining industry, 2018-2029

This study forecast the copper mining industry’s electricity consumption in 2018-2029, it would increase from 22.6 to 31.9 TWh (~2.9% average annual growth). In this case, it isestimated that, in order to satisfy the copper mining industry’s demand, it would be necessary to add 1,336 MW in generating capacity between 2019 and 2029. The Antofagasta Region will continue to account for over half of the industry’s consumption, followed by the Atacama, Tarapacá and O'Higgins Regions. However, as from 2024, the share of the Atacama Region is expected to reach over 13% while that of the O’Higgins Region drops to 7%. The Tarapacá Region would show no significant change. The concentration is by far the most important source of expected consumption of electricity throughout the period, accounting for 58% in 2018 and 67% by 2029. The use of seawater is another process where electricity consumption is expected to show an important increase, rising from 4% of total consumption in 2018 to 10% in 2029 and emerging as the second most electricityintensive process. The existing operations in 2018 explain practically all the copper mining industry’s expected electricity consumption but, by 2029, potential, possible and probable projects would account for close to a quarter of its consumption. The new projects will acquire growing importance, accounting for 56% of expected consumption by 2029, up from 19% in 2018.

Short-Term Forecasting of Total Energy Consumption for India-A Black Box Based Approach

Continual energy availability is one of the prime inputs requisite for the persistent growth of any country. This becomes even more important for a country like India, which is one of the rapidly developing economies. Therefore electrical energy’s short-term demand forecasting is an essential step in the process of energy planning. The intent of this article is to predict the Total Electricity Consumption (TEC) in industry, agriculture, domestic, commercial, traction railways and other sectors of India for 2030. The methodology includes the familiar black-box approaches for forecasting namely multiple linear regression (MLR), simple regression model (SRM) along with correlation, exponential smoothing, Holt’s, Brown’s and expert model with the input variables population, GDP and GDP per capita using the software used are IBM SPSS Statistics 20 and Microsoft Excel 1997–2003 Worksheet. The input factors namely GDP, population and GDP per capita were taken into consideration. Analyses were also carried out to find the important variables influencing the energy consumption pattern. Several models such as Brown’s model, Holt’s model, Expert model and damped trend model were analysed. The TEC for the years 2019, 2024 and 2030 were forecasted to be 1,162,453 MW, 1,442,410 MW and 1,778,358 MW respectively. When compared with Population, GDP per capita, it is concluded that GDP foresees TEC better. The forecasting of total electricity consumption for the year 2030–2031 for India is found to be 1834349 MW. Therefore energy planning of a country relies heavily upon precise proper demand forecasting. Precise forecasting is one of the major challenges to manage in the energy sector of any nation. Moreover forecasts are important for the effective formulation of energy laws and policies in order to conserve the natural resources, protect the ecosystem, promote the nation’s economy and protect the health and safety of the society.

Hybrid simulation method by cooperating between manufacturing system simulation and computational fluid dynamics simulation (Evaluation for simultaneous operation of production lines and compressed air systems, and evaluation for energy consumption per unit of production throughput)

For enhancing the energy efficiency of a manufacturing system as a whole, it is critical to optimize energy consumption including that in the utility systems, which supplies compressed air, and that in each production line. Compressed air is used in the manufacturing domain in a wide variety of systems such as those that fix and hold works through pneumatic equipment. Compressed air systems account for approximately 10% of the industrial electricity consumption in the European Union. Therefore, a method for minimizing the energy used in compressed air systems is sought. From the point of view of energy saving in the operation of a compressed air system, a method is available for reducing the output of the compressed air system. However, if the supply of compressed air becomes insufficient by the reduction in the amount supplied, it can result in extension of the cycle time of the production line or in defective products; thus, it can eventually cause lower productivity. Therefore, businesses ensure adequate supply from compressed air systems in order to prevent the adverse impact of insufficient supply of compressed air on productivity; however, this results in wastage of energy. Therefore, it is necessary to simultaneously evaluate productivity and compressed air feed. However, there has been no progress in the research of methods for evaluation. Hence, in this research, a method, which is a hybrid of a manufacturing system simulation and computational fluid dynamics simulation, is proposed for evaluating the operation of both production lines and compressed air systems simultaneously. The proposed method is used to evaluate the deficiencies and excesses in the supply from a compressed air system and the reliability of the supply of a compressed air system in response to the daily production schedule. Moreover, the energy consumption per unit of production throughput is evaluated.

Industrial power demand forecasting based on big data technology orienting to energy internet: A case study of Hunan Province

Making energy carry information is the major research direction of achieving Energy Internet. Besides, electric energy has incomparable advantages in energy transmission efficiency. Therefore, the future of Energy Internet is the Power Internet. Then, accurately predicting the consumption of power becomes the foundation of Energy Internet. Depending on the extraction, transformation, loading (ETL), Hadoop, Oracle and OLAP technologies. This paper establishes energy, electricity, economy forecasting and warning system. By considering the data of energy, electricity and economy together, a new economic power transmission model is established. The traditional econometric methods, such as OLS, AR, MA, ARMA and X11, are all employed during the estimated process. The estimating results demonstrate that the goodness of fits of the new models are all approximately equal to 0.998. The electricity consumption of industry is 1935224.28*104 kWh in the 3rd quarter and 2,846,897.0 *104 kWh in the 4th in 2017, respectively.

Modeling the multiple benefits of electricity savings for emissions reduction on power grid level: A case study of China’s chemical industry

Industry is a large electricity user. China’s chemical industry (globally the largest based on sales) contributes 7% to China’s GDP, while it consumes 11% of the total electricity consumption in industry and is responsible for 40% of total CO2eq, 40% of SO2, 59% of NOx and 18% of PM-emissions of the chemical industry emissions. The heterogeneity of GHG and air pollutant emissions across electricity grids (within a country) is rarely included in analyses. In this paper, electricity conservation supply curves are developed (distinguishing the grids) to estimate the cost-effective and technical potentials of electricity conservation in China’s chemical industry. The emission factors per grid for GHG (i.e. CO2, CH4 and N2O) and air pollutants (i.e. SO2, NOx and PM2.5) are calculated and used to quantify the emissions mitigation achieved by electricity saving technologies in the chemical industry for the period 2012–2035. Results show that significant multiple benefits can be obtained by implementing electricity efficiency measures. There are large differences among the six grids in terms electricity savings and emissions abatement of GHG and air pollutants. 83% of the total electricity saving potential is contributed by the North, Northwest and Central grids, equal to 32% of baseline electricity consumption in 2035. In 2035, 129 Mt of CO2, 33 kt CO2eq of CH4, 571 kt CO2eq of N2O, 235 kt of SO2, 275 kt of NOx and 52 kt of PM2.5 in these three grids can be avoided as a result of electricity savings (a reduction of 31–33% compared to baseline emissions). When decision-makers set targets for energy saving and emission reduction, the multiple benefits and grid heterogeneity should not be ignored.

Effective sparse adaboost method with ESN and FOA for industrial electricity consumption forecasting in China

Accurate electricity consumption forecasting is a challenging task for its unstable behavior and influence mechanism based on multiple factors. In this study, a neural network ensemble approach is designed to solve this problem. In the proposed method, a novel sparse adaboost (adaboostsp) is designed as the ensemble framework to enhance the generalization ability and reduce ensemble cost, and echo state network (ESN) is adopted to build the nonlinear relationships between electricity demand and multiple factors. An improved fruit fly optimization algorithm (FOA) helps selecting input variables considering their time lag effects. Two industrial electricity consumption (IEC) forecasting applications in China are investigated to verify the effectiveness of proposed ensemble forecasting approach. Numerical results indicate that adaboostsp-ESN with FOA can better predict the future IEC than various benchmark methods. Compared with existing boosting ensemble approaches, the proposed adaboostsp is more efficient and can save considerable computation cost. Impacts of selected variables are further examined and results show many industrial indexes have significant time lag effects on IEC. Based on the proposed techniques, future IEC demand in Hubei Province is estimated and analyzed. Application studies demonstrate the proposed hybrid ensemble approach is a practical choice for mid-term IEC adjustment and projection.

Investment incentives for flexible demand options under different market designs

This paper analyzes investment incentives for flexible manufacturing facilities under different market designs. We propose a multi-stage equilibrium model incorporating endogenous determination of generation capacity investment, network expansion and redispatch based on the model introduced by Grimm et al. (2016), including flexibilization of industrial electricity consumption. The model allows to investigate incentives for flexibilization and the impact of flexible industrial electricity consumers on the system. An application to the German electricity market reveals that flexible industrial electricity consumption can be profitable for firms. If the share of flexible electricity consumers is high, price fluctuations are mitigated, which lowers the individual cost savings from demand flexibility. Comparing different market designs, positive impacts of flexible electricity demand on the system are observed in both the system optimum and the market equilibrium. In scenarios with flexible industrial electricity consumption, welfare is considerably higher than in those without. This is due to lower electricity costs of industrial consumers, but more importantly due to less investment in conventional power generation as well as a reduced transmission network expansion. However, a comparison of nodal and uniform pricing underlines the importance of regional price signals with respect to an efficient allocation of flexible industrial demand.

Does line loss broaden the deviation between the added value of industry and the industrial electricity consumption in China?

Studies have shown that industrial structure (IS) adjustment, climate factors, energy intensity, etc. factors are the reasons causing the deviation between the growth rate of industrial value added (IVA) and electricity consumption of industry (Ele) in China. However, no one researches whether line loss (LL), calculated in industrial electricity consumption in China, is one of the reasons contributing to the deviation. In order to thoroughly figure out what is the cause of the deviation, theoretical method, correlation analysis, autoregressive distributed lag (ARDL) model and an error correction model (ECM) have been undertaken in the paper. Experimental results are as follows: (1) LL fluctuates violently in accordance with temperature, while IVA is not; (2) the correlation between IVA and Ele becomes stronger, if LL is eliminated from Ele; (3) both in the long-run and short-run equation of ARDL model, IS is the first driver of Ele, followed by LL and IVA; (4) compared with International Energy Agency and based on the experimental results, the practice statistics of LL in Ele is not reasonable in China; (6) policies for promoting the upgrading of the internal industrial structure and for improving the statistical method of electricity consumption in China are proposed, finally.

Forecasting China's electricity consumption using a new grey prediction model

A modified grey prediction model is employed to accurately forecast China's overall and industrial electricity consumption. To this end, a novel optimized grey prediction model, combining a new initial condition and rolling mechanism, is designed on the principle of “new information priority”. The previous initial conditions possess the inherent deficiencies of having a fixed structure and poor adaptability to changing raw data. To overcome these deficiencies, the new initial condition, possessing alterable weighted coefficients, is proposed. Its generating parameters can be optimally determined by employing a particle swarm optimization algorithm according to various characteristics of the input data. In addition, to demonstrate its efficacy and applicability, the novel model is utilized to predict China's total and industrial electricity consumption from 2012 to 2014 and then compared to forecasts obtained from a range of benchmark models. The two empirical results illustrate that the novel initial condition with dynamic weighted coefficients can better adjust to the features of electricity consumption data than the previous initial conditions. They also show the superiority of the newly proposed model over the benchmark models. Within this paper, the new model is used for predicting the future values of China's total and industrial electricity consumption from 2015 to 2020.

The Recurrence Interval Difference of Power Load in Heavy/Light Industries of China

The significant fluctuation of industrial electricity consumption has a high impact on power load, which makes the research on recurrence intervals between extreme events of theoretical and practical significance. The study uses a high-frequency data of heavy and light industries and employs recurrence interval analysis in different thresholds. We find that the reoccurrence interval of volatility can fit with the stretched exponential function and the probability density functions of recurrence intervals in various thresholds shows a scaling behavior. Then, the conditional probability density function and the multifractal detrended fluctuation analysis demonstrate the existence of short-range correlation, long-range correlation, and multifractal properties, respectively. We further construct a hazard function, introduce recurrence intervals into VaR calculation and establish a functional relationship between average recurrence interval and threshold. Following this result, we also shed light on policy discussion for multi-industrial electricity supply management.

Forecasting the Electricity Demand and Market Shares in Retail Electricity Market Based on System Dynamics and Markov Chain

Due to the deregulation of retail electricity market, consumers can choose retail electric suppliers freely, and market entities are facing fierce competition because of the increasing number of new entrants. Under these circumstances, forecasting the changes in all market entities, when market share stabilized, is important for suppliers making marketing decisions. In this paper, a market share forecasting model was established based on Markov chain, and a system dynamics model was constructed to forecast the electricity consumption based on the analysis of five factors which are economic development, policy factors, environmental factors, power energy substitution, and power grid development. For a real application, the retail electricity market of Guangdong province in China was selected. The total, industrial, and commercial electricity consumption in Guangdong from 2016 to 2020 were predicted under different scenarios, and the market shares of the main market entities were analyzed using Markov chain model. Results indicated that the direct trading electricity would account for 70% to 90% of the total electricity consumption in the future. This provided valuable reference for the decision-making of suppliers and the development of electricity industry.

剥离“线损电量”的工业用电量统计方法研究——解析工业用电与增加值增速偏离之谜

剥离“线损电量”的工业用电量统计方法研究——解析工业用电与增加值增速偏离之谜

A New Field Test Method for Determining Energy Efficiency of Induction Motor

Electric motors and the drive systems approximately consume half of the global electricity consumption. In the European Union (EU), electric motors and the drive systems are estimated to account for about 70% of all industrial electricity consumption. Therefore, mandatory applications accrue from the legislation of governments or unions like the EU for customs and market surveillance or eco-design requirements. The mandatory applications and the global market size find out the necessity of efficiency measurement. There are several compliance and precompliance efficiency test methods in the literature. The compliance tests should execute reliable, accurate, and reproducible results. The purpose of this paper is to define a simple field method that will be used for measuring and determining in-service efficiency at industrial conditions. The proposed method in this paper is an inexpensive and easy method that would help to energy managers and engineers to make correct decision in replacing inefficient motors with efficient and new ones. In this paper, a novel induction motor-efficiency model function has been proposed based on the International Electrotechnical Commission 60034-2-1 standard equations, and a new field test procedure has been used in efficiency estimation. The new efficiency model function variables and constants are determined from the regression analysis, which depends on 86 tests of 437 different load points’ efficiency data. Furthermore, the proposed field test procedure, equations, and compliance test methods are tested on a sample motor in laboratory, and the results are compared to each other.

Industrial electricity consumption, human capital investment and economic growth in Chinese cities

We examine the cointegrating and Granger causal relationships between economic growth, industrial electricity consumption and human capital in China using a panel consisting of 210 prefecture cities across a period of 2003–2012. Applying continuously-updated fully modified panel estimation, we find that industrial electricity consumption has a significantly larger impact on economic prosperity than physical and human capital investment. Industrial electricity consumption plays a dominant role to boost growth in the center and the west, while human capital contributes most to the growth of the east. And notably, core cities are the largest beneficiaries from the growth of production inputs. Accounting for heterogeneity and cross-section dependence, results from the Granger non-causality tests suggest that the implementation of electricity-saving strategy is more feasible in inland China without hampering the economic growth and development of human capital. In the east, regulators must make efforts to increase investment in building electricity infrastructure to connect demand with supply to avoid shortage of power having an adverse effect on economic growth. Reducing the urban-rural gap in electricity tariffs and improving development of rural electricity network are rationalized to achieve sustainable economic development for the urban community in China.

Carbon and Energy Saving Financial Opportunities in the Industrial Compressed Air Sector

The transition towards a more sustainable energy scenario calls for both medium-to-long and short term interventions, with CO2 reduction and fossil fuel saving as main goals for all the Countries in the World. Among all others, one way to support these efforts is the setting-up of immaterial markets able to regulate, in the form of purchase and sales quotas, CO2 emissions avoided and fossil fuels not consumed. As a consequence, the upgrade of those sectors, characterized by high energy impact, is currently more than an option due to the related achievable financial advantage on the afore mentioned markets. Being responsible for about 10% electricity consumption in Industry, the compressed air sector is currently addressed as extremely appealing, when CO2 emissions and burned fossil fuels saving are in question. In the paper, once a standard is defined for compressors performances, based on data from the Compressed Air and Gas Institute and PNEUROP, the achievable energy saving is evaluated along with the effect in terms of CO2 emissions: with reference to those contexts in which mature intangible markets are established, an estimation of the financial benefit from savings sale on correspondent markets is possible, in terms of both avoided CO2 and fossil fuels not burned. The approach adopted allows to extend the analysis results to every context of interest, by applying the appropriate emission factor to the datum on compressor specific consumption.

Analysis of Industrial Electricity Consumption Flexibility. Assessment of Saving Potential in Latvia and Kazakhstan

There are several methods to reduce non-renewable resources use and increase share of renewable energy sources. As the main targets are to minimize greenhouse gas emissions till 2020 and 2030, industry and other sectors need to figure out the most suitable solutions for emission reduction. Demand response (DR) application for industry sector can reduce electricity consumption. The aim of this research is to find the positive effect of DR use for food industry and compare demand response potential in Latvia and Kazakhstan. The obtained results enable one to analyse the temporal variability of the overall demand response potential.

A Study of the Transfer Entropy Networks on Industrial Electricity Consumption

We study information transfer routes among cross-industry and cross-region electricity consumption data based on transfer entropy and the MST (Minimum Spanning Tree) model. First, we characterize the information transfer routes with transfer entropy matrixes, and find that the total entropy transfer of the relatively developed Guangdong Province is lower than others, with significant industrial cluster within the province. Furthermore, using a reshuffling method, we find that driven industries contain much more information flows than driving industries, and are more influential on the degree of order of regional industries. Finally, based on the Chu-Liu-Edmonds MST algorithm, we extract the minimum spanning trees of provincial industries. Individual MSTs show that the MSTs follow a chain-like formation in developed provinces and star-like structures in developing provinces. Additionally, all MSTs with the root of minimal information outflow industrial sector are of chain-form.

Effects of Urbanization on Rural Drinking Water Quality in Beijing, China

Urbanization is an inevitable trend in historical development, but eco-environmental problems, including drinking water safety, have gradually become more and more outstanding during the process of rural urbanization. Ten districts in rural areas of Beijing, China were selected to study the effects of urbanization on drinking water quality. The relation between the urbanization index and drinking water quality indicators were explored. The influence of the urbanization process on drinking water quality showed that housing construction, population urbanization, energy consumption, and industrialization during urban development were closely related to drinking water quality. The paired t-test showed the total electricity consumption, living electricity consumption, tertiary industry, and the GDP growth rate had boundary (p = 0.06) or significantly positive (p < 0.05) relations with the qualified rate of rural drinking water. The grey correlation analysis showed that the growth rates of the value-added of housing construction areas were the most important factor affecting comprehensive water quality of Beijing rural areas, followed by the growth rates of the value-added by secondary industry and total electricity consumption, and then the growth rates of the value-added by the tertiary industry and GDP. Urbanization had a significant impact on individual water quality indicators. The results of this study provided some supports for drinking water security in the face of urbanization.

Forecasting electricity demand for Turkey: Modeling periodic variations and demand segregation

In deregulated electricity markets the independent system operator (ISO) oversees the power system and manages the supply and demand balancing process. In a typical day the ISO announces the electricity demand forecast for the next day and gives participants an option to prepare offers to meet the demand. In order to have a reliable power system and successful market operation, it is crucial to estimate the electricity demand accurately. In this paper, we develop an hourly demand forecasting method on annual, weekly and daily horizons, using a linear model that takes into account the harmonics of these variations and the modulation of diurnal periodic variations by seasonal variations. The electricity demand exhibits cyclic behavior with different seasonal characteristics. Our model is based solely on sinusoidal variations and predicts hourly variations, without using any climatic or econometric information. The method is applied to the Turkish power market on data for the period 2012–2014 and predicts the demand over daily and weekly horizons within a 3% error margin in the Mean Absolute Percentage Error (MAPE) norm. We also discuss the week day/weekend/holiday consumption profiles to infer the proportion of industrial and domestic electricity consumption.