Regional Electricity Consumption Research Articles

Forecasting regional industrial production with novel high‐frequency electricity consumption data

AbstractIn this paper, we study the predictive power of electricity consumption data for regional economic activity. Using unique high‐frequency electricity consumption data from industrial firms for the second‐largest German state, the Free State of Bavaria, we conduct a pseudo out‐of‐sample forecasting experiment for the monthly growth rate of Bavarian industrial production. We find that electricity consumption is the best performing indicator in the nowcasting setup and has higher accuracy than other conventional indicators in a monthly forecasting experiment. Exploiting the high‐frequency nature of the data, we find that the weekly electricity consumption indicator also provides good predictions about industrial activity in the current month with only 2 weeks of information. Overall, our results indicate that regional electricity consumption is a promising avenue for measuring and forecasting regional economic activity.

Application of grey system model with intelligent parameters in predicting regional electricity consumption

PurposeGiven the regional heterogeneity of economic development, electricity consumption in various regions exhibits a discrepant growth pattern. The purpose of this study is to construct a grey system forecasting model with intelligent parameters for predicting provincial electricity consumption in China.Design/methodology/approachFirst, parameter optimization and structural expansion are simultaneously integrated into a unified grey system prediction framework, enhancing its adaptive capabilities. Second, by setting the minimum simulation percentage error as the optimization goal, the authors apply the particle swarm optimization (PSO) algorithm to search for the optimal grey generation order and background value coefficient. Third, to assess the performance across diverse power consumption systems, the authors use two electricity consumption cases and select eight other benchmark models to analyze the simulation and prediction errors. Further, the authors conduct simulations and trend predictions using data from all 31 provinces in China, analyzing and predicting the development trends in electricity consumption for each province from 2021 to 2026.FindingsThe study identifies significant heterogeneity in the development trends of electricity consumption systems among diverse provinces in China. The grey prediction model, optimized with multiple intelligent parameters, demonstrates superior adaptability and dynamic adjustment capabilities compared to traditional fixed-parameter models. Outperforming benchmark models across various evaluation indicators such as root mean square error (RMSE), average percentage error and Theil’s index, the new model establishes its robustness in predicting electricity system behavior.Originality/valueAcknowledging the limitations of traditional grey prediction models in capturing diverse growth patterns under fixed-generation orders, single structures and unadjustable background values, this study proposes a fractional grey intelligent prediction model with multiple parameter optimization. By incorporating multiple parameter optimizations and structure expansion, it substantiates the model’s superiority in forecasting provincial electricity consumption.

Decomposition Analysis of Regional Electricity Consumption Drivers Considering Carbon Emission Constraints: A Comparison of Guangdong and Yunnan Provinces in China

Electricity consumption is closely linked to economic growth, social development, and carbon emissions. In order to fill the gap of previous studies on the decomposition of electricity consumption drivers that have not adequately considered carbon emission constraint, this study constructs the Kaya extended model of electricity consumption and analyzes the effects of drivers in industrial and residential sectors using the Logarithmic Mean Divisia Index (LMDI) method, and empirically explores the temporal and spatial differences in electricity consumption. Results show that: (1) During 2005–2021, the total final electricity consumption growth in Guangdong was much higher than that in Yunnan, but the average annual growth rate in Guangdong was lower, and the largest growth in both provinces was in the industrial sector. (2) The labor productivity level effect is the primary driver that increases total final electricity consumption (Guangdong: 78.5%, Yunnan: 87.1%), and the industrial carbon emission intensity effect is the primary driver that decreases total final electricity consumption (Guangdong: −75.3%, Yunnan: −72.3%). (3) The year-to-year effect of each driver by subsector is overall positively correlated with the year-to-year change in the corresponding driver, and declining carbon emission intensity is a major factor in reducing electricity consumption. (4) The difference in each effect between Guangdong and Yunnan is mainly determined by a change in the corresponding driver and subsectoral electricity consumption. Policy implications are put forward to promote energy conservation and the realization of the carbon neutrality goal.

Intelligent anomaly detection method for power and electricity data based on cubic exponential smoothing model

During the operation of the power system, abnormal electricity data are inevitably generated. How to efficiently detect and identify these abnormal data is a crucial component of power system state estimation, and it is also the foundation for the safety and stability of power system operation. Traditional anomaly detection methods only focus on specific power system characteristics, and there are problems with high computational complexity and low accuracy. To address the shortcomings of traditional methods for anomaly detection in power and electricity data, a smart anomaly detection method for power and electricity data based on a cubic exponential smoothing model is proposed. The cubic exponential smoothing model uses historical data to predict the current regional electricity consumption and then subtracts the predicted value from the true value to obtain the residual term. Finally, the DBSCAN density clustering algorithm is used to cluster the residual term, achieving the recognition of abnormal electricity data. We conduct an experimental comparison of regional electricity consumption data of a certain power grid. The results indicate that the proposed method has achieved good results in both detection rate and false alarm rate indicators in the intelligent detection of abnormal data in power and electricity consumption.

The allometric growth relationship between electricity consumption and economics in China.

Electric power is the basic industry of a modern country. The rapid development of the power industry has promoted economic development and social progress. With the establishment of the carbon neutrality target of carbon peak, China's power industry is also facing new situations and new challenges. This paper innovatively introduces the concept of allometric growth in biology, and uses the data of provincial electricity consumption and economic development from 1972 to 2017, to study the allometric growth relationship between electricity consumption and economics in China at multiple scales. Combined with the spatial autocorrelation analysis method, the temporal and spatial characteristics of allometric growth between electricity consumption and economic development were researched based on the analysis of spatial correlation of GDP electricity consumption intensity. The results show that: (1) The provincial GDP electricity consumption intensity shows an aggregate distribution, and the local aggregate pattern changed significantly from 1972 to 2017. (2) The High-High Cluster gradually disappeared, and the Low-Low Cluster moved southward, appearing in the middle and lower reaches of the Yangtze River. (3) The differential growth coefficient of electricity consumption and economic development increased initially and decreased from 1972 to 2017. (4) GDP would increase by 7.4%, 20.1%, and 15.6% in the simulation of electricity consumption, increasing 10% at the three stages of China's economic development. (5) The allometric growth coefficients of electricity consumption and economic development are very different in the three economic belts, eight economic zones, and provinces with different characteristics at the different stages of economic development. Formulating differentiated regulation policies for regional economic development and electricity consumption will be conducive to the coordinated development of electric power production and the national economy.

Has green finance boosted regional electricity consumption? Based on nighttime light data.

Green finance is gradually becoming essential to enhancing regional electricity consumption. This paper uses panel data from 30 Chinese provinces and regions from 2011 to 2021 and develops a system Generalized Method of Moments (GMM) and a mediating effect model to test the effects of green finance on regional electricity consumption level and the mechanism of action. The results show that green finance significantly boosts regional electricity consumption level, which still holds after various robustness tests. In terms of heterogeneity, the promotion effect of green finance on electricity consumption is significantly more substantial in the Eastern regions than in the central and western regions; the regions with a higher level of urbanisation are more robust than those with a lower level of urbanisation, and the regions with a higher level of the advanced industrial structure are stronger than those with a lower level. In terms of impact mechanisms, both foreign direct investment and green technology innovation have partial mediating effects on the impact of green finance on regional electricity consumption level. Green finance raises the level of regional electricity consumption by increasing the level of green technology innovation. At the same time, foreign direct investment has a "substitution effect" on the impact of green finance on regional electricity consumption. Therefore, it is essential to increase green financial policy support to raise regional electricity consumption, help upgrade electricity consumption, and help achieve the "double carbon goal".

Who shapes the embodied carbon dioxide emissions of interconnected power grids in China? A seasonal perspective

With the rapid development of interregional power transmission, the redistribution of fossil and renewable energy resources has changed sharply, and its complexity poses a challenge to the evaluation of power carbon emission responsibility. This study constructs an interprovincial power transmission framework to measure the seasonal carbon emissions embodied in regional electricity consumption over the period of 2008–2015 based on quarterly data. Then, a structural decomposition approach was developed to identify the influential factors of carbon emissions embodied in provincial electricity consumption from a seasonal perspective. The results show that the assessment for embodied emissions of power consumption based on different levels of data may vary by as much as 20%, and the carbon emissions and carbon intensity of power consumption exhibit significant seasonal characteristics. Furthermore, it is revealed that the economic scale in the fourth quarter makes the most significant contribution to the emissions increment, especially in underdeveloped provinces, while the change in energy efficiency of power generation reduces more carbon emissions in the first and second quarters. In addition, the impact of the power transmission scale is more significant in the third and fourth quarters, and it has been close to or even more than the impact of traditional factors in some quarters. Finally, the impact of economic scale, power generation energy intensity, power generation mix and electricity utilization efficiency on the emissions of regional power grids shows a relatively stable increasing trend, but this trend of directional stability is not reflected in the effect of the power transmission structure and transmission scale. This study contributes to the identification of the impact of the power transmission structure and transmission scale. Moreover, this study highlights the importance of considering seasonal characteristics when estimating the carbon emissions of power consumption and formulating specific emission reduction policies. Additionally, it provides a more accurate evaluation of carbon emissions and proposes several prominent recommendations for policy makers.

Smart Meter Data Obfuscation With a Hybrid Privacy-Preserving Data Publishing Scheme Without a Trusted Third Party

Smart electricity meters as a prominent instance of the Internet of Things (IoT) have driven more efficient energy services in smart grids but also created growing concerns of consumer privacy. Homomorphic encryption of consumption data is a conventional solution for privacy protection, but it incurs a high computational burden to the resource-restrained smart meters (SMs) due to the encryption of high-frequency consumption readings. Perturbation is another major approach in providing the privacy protection of SM readings which is highly efficient. However, most existing perturbation-based works inadequately balance the tradeoff between the need of hiding individual consumption profiles and the need of retaining utility’s quality. This article proposes a hybrid privacy-preserving electricity consumption data publishing scheme without a trusted third party which utilizes the desirable properties of both perturbation and cryptography for better privacy-utility tradeoff and efficiency. The proposed scheme for fine-grained SM consumption data consists of two phases, which are noise generation and noise distribution. In the first phase, a distributed perturbation method is designed to provide differential privacy protection for high-frequency consumption while retaining the accuracy of energy services like regional load forecasting. In the second phase, a private noise distribution protocol, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$nn$ </tex-math></inline-formula> -PND, securely distributes <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> noise elements generated by an energy distribution operator to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> SMs in a semihonest adversarial model. Formal proofs of correctness and privacy of the scheme are provided. Experiments of regional short-term electricity consumption forecast using real-world data sets demonstrate the preservation of the utility over the masked data of this scheme.

Green Energy Development in Russian Regions

The analysis of the development of “green energy” in the regions of Russia is one of the pressing modern scientific issues. This article describes the general situation with carbon dioxide emissions in the atmosphere in the world and in Russia. It is shown that the Russian Federation is one of the world leaders in the volume of carbon dioxide emissions. Global and Russian trends in the development of renewable energy sources are considered. The spatial unevenness in the development of renewable energy sources in the regions of the Russian Federation is characterized. An econometric model is proposed, which investigates the influence of the production process (through such factors as gross regional product and electricity consumption) as well as the development of renewable energy sources on carbon dioxide emissions in Russian regions. The results show that gross regional product and regional electricity consumption have positive impact on increasing carbon dioxide emission while the increase of renewable energy sources has a negative impact on increasing carbon dioxide emission. At the present stage, Russian regions can face a number of problems and constraints of social and economic nature that may hinder the development of a “green economy”.

The propellant role of the mega-grid in regional economic growth: Evidence from China base on the panel smooth transition regression model

One of the secrets for China's economic growth miracle is continuous investment in infrastructure, and the role of power infrastructure is irreplaceable. However, previous studies mostly focused on generation-side indicators rather than grid-side indicators, ignoring the importance of the mega-grid established in China. As a result, this paper is committed to exploring the economic growth pulling effect of power grids with different voltage levels in the nonlinear framework. On the premise of determining the robustness of estimation parameters by instrumental variable method, the panel smooth transition regression (PSTR) model is used to capture panel heterogeneity. To be exact, the regional economic growth driven by extra-high voltage (EHV) and ultra-high voltage (UHV) power grids presents a regime transition accompanied by the growth of regional power generation and electricity consumption per capita. This means that the mega-grid is more conducive to the economic growth of energy bases and load centers. To further build and improve the state-leveled mega-grid into a nationwide basic platform for trans-regional renewable energy transmission and distribution is a key measure to enhance China's sustainable economic growth under the constraints of carbon peak and carbon neutrality targets.

Spatiotemporal Evolution and Influencing Factors of Electricity Consumption in the Yangtze River Delta Region

Electricity consumption accounts for a considerable part of the final energy consumption, and it is important for economic development and human life. This study explores the spatiotemporal evolution characteristics and influencing factors of electricity consumption in the Yangtze River Delta region in China from 2006 to 2019, using the gravity model and Logarithmic Mean Divisia Index method, respectively. The results show that: (1) The centers of gravity for the total final, industrial and residential electricity consumptions have a trend of migration towards the west. (2) The distance of migration of the center of gravity for residential electricity consumption is the highest, and the trend of migration of the center of gravity for industrial and total final electricity consumptions are synchronous. (3) Economic development is the main reason for the growth in regional electricity consumption, and the decrease in the investment electricity consumption intensity inhibits the growth of electricity consumption. This study provides references to restrain the excessive increase in electricity consumption and improve the layout of power facilities at the regional level.

A Short Term Forecasting Method for Regional Power Consumption Considering Related Factors

Analysis and prediction of power consumption law is the basis of power grid planning and construction, and is also an effective guide for energy demand side management. With the rapid development of economy and the complex change of industrial structure in recent years, the internal structure of power demand is changing to some extent. Therefore, a short-term forecasting method of regional electricity consumption considering the related factors is proposed. Based on the analysis results, a short-term prediction model of regional electricity consumption considering the related factors is established, and the short-term prediction is realized by the calculation of the model. Through the example analysis, it is verified that the forecasting deviation of the short-term forecasting method is low and meets the basic requirements of electric quantity forecasting.

Рост электропотребления российских регионов как фактор их социально-экономического развития

The indicator of specific (per capita) electricity consumption (SEC) is stabilising in developed countries and increasing in developing economies. At least since the mid-2000s, the difference between the two groups of countries in terms of available power has been decreasing. In contrast to this trend, the transition of Russia to a market economy is characterised by the divergence of regions in terms of SEC. SEC of regions, being on average similar to that of the Netherlands, France, Germany, Great Britain, began to differ by 20 times. The technocenosis theory shows the need to change this negative trend. The low electricity consumption of developing countries depends on their inability to increase its production. In Russia, the problem is due to the low potential of the consumption sector, primarily in regions with SEC below 4 MWh/person per year. The solution of this macroeconomic problem, namely, the reduction of the existing differentiation in regional specific electricity consumption will help maintain the structural stability of the Russian economy and ensure its functioning in the context of external environment changes. Creation of conditions for the energy consumption development in industrial and agricultural enterprises is not a sectoral task of the electric power industry. Considering the sanctions imposed on Russia, structural stability of the national economy can be increased by improving the investment climate resulting from a decline in electricity prices for new non-residential consumers in regions with SEC below the level of developing countries. For these subsidy depending “outsider” regions, an increase in the availability of electricity, rather than the construction of new energy facilities, stimulates the labour productivity growth, re-industrialisation, and emergence of growth points.

Decomposition the driving force of regional electricity consumption in Japan from 2001 to 2015

Investigating electricity consumption is vital for identifying the driving forces for national onsite energy use and pathways for mitigating greenhouse gas emissions. However, before any electricity conservation measures are implemented, a well understanding of the driving forces behind sectoral electricity consumption variation are of great importance. Therefore, this research constructed an assessment framework on how electricity consumption reflects and responds to social and natural events, and how far the Japanese electricity consumption decouples economic development by estimating the changes in electricity consumption in Japan from 2001 to 2015. The results revealed that electricity consumption intensity and economic activity effect were both dominant but obtained opposite impacts in terms of electricity consumption changes. Moreover, differences in driving forces that caused spatial variance for regional changes in electricity consumption were found during the economic recession and recovery periods. The decoupling status showed that the decoupling degree in Japan changed from an extensive negative decoupling state to a weak decoupling state during the study periods. Subsequently, the results also shed light on how to mitigate regional electricity consumption stress sustainably.

Two types of conformable fractional grey interval models and their applications in regional electricity consumption prediction

Taking the unbalance development inside a region into consideration, it will be better to express the regional electricity consumption (EC) value as an interval number to preserve more complete information. However, there are rare papers about this interesting topic. For the small amount of EC data, grey interval prediction model is employed in this paper. However, the existing models are almost all integer-order grey interval models and based on area coordinate conversion method. In order to fill this gap, and to obtain more accurate forecasting results, a conformable fractional non-homogenous discrete grey model (CFNDGM(1,1,α)) is proposed, and on the basis of CFNDGM model, two conformable fractional grey interval models are built. One is based on area coordinate conversion method (CFNDGM_AC), and the other is with information decomposition conversion method (CFNDGM_ID). The mathematical relationship of the two types of grey interval models is firstly given in this paper. It indicates that which type of model has better performance depends on the characteristics of original data. To assess the two fractional grey models, annal EC values in southern Jiangsu are taken as an example, and other four grey interval models are taken for comparison. Results show that CFNDGM_ID has the best performance among six models in both simulation and prediction. Then CFNDGM_ID is chosen to predict EC of southern Jiangsu in the next five years. To further improve forecasting accuracy, the optimal fitting size of CFNDGM_ID is selected. Forecasting results show that the EC in southern Jiangsu will increase in the next five years, but at a lower growth rate.

How the uptake of electric vehicles in China leads to emissions transfer: An Analysis from the perspective of inter-provincial electricity trading

The rapid growth of electric vehicles (EVs) has created an unprecedented demand for electricity, and the generation of the electricity required to meet this demand leads, in turn, to large emissions. Interregional electricity trading is a general way of solving the imbalance between regional electricity generation and consumption, and it has profoundly affected the emission patterns of EVs. This study used a Quasi-Input-Output model to analyze the transferred emissions caused by the electricity consumption of EVs in China, the world's largest EV market, from the perspective of inter-provincial electricity trading. The results showed that the amount of transferred GHG, SO2 and NOx emissions caused by EV promotion in 18 selected provinces in 2017 were 1,979.6 kt, 1093.71 t and 1007.68 t, respectively. The main pathways were emissions transferred from the developed east of China to the less developed midwest. Apart from direct emissions transfers between provinces with electricity trade relationships, EVs can produce indirect emissions transfers due to the connectivity of the national electricity network; this part accounts for less than 10% of the total transferred emissions in most provinces. In addition, transferred emissions are an important component of the total emissions of the EV driving stage. From a whole-country perspective, the results of a cost–benefit analysis showed that interregional electricity trading is conducive to improving the national environmental benefits of EV promotion. This study provides an empirical basis for understanding the embodied emissions due to the uptake of EVs, thereby providing implications for promoting the collaborative development of EVs and facilitating comprehensive management of regional emissions transfers.

Research on the driving effect of production electricity consumption changes in the Yangtze River Economic Zone - Based on regional and industrial perspectives

In this study, the LMDI method is used to decompose the production power consumption changes of the Yangtze River Economic Zone into an advanced technology effect, structural effect, income effect, and population effect. The regional and industry differences in the driving effect of electricity consumption change are also investigated. From the perspective of time differences, the progress of power-saving technology is the main factor restraining the growth of electricity consumption; whereas, economic growth, industry structure adjustment, and population size adjustment promote the growth of electricity consumption; and the other three influencing factors, in turn, decrease. From the perspective of regional differences, regional electricity consumption varies significantly, and various regions have great room for improvement in industry structure. Regional population affects electricity consumption from two aspects: absolute population quantity change and population mobility. From the perspective of industry differences, electricity consumption varies significantly among industries and is mainly reflected in the effect of technological progress and structural effects. As a result, control measures on electricity consumption in the Yangtze River Economic Zone should focus on the promotion of power-saving technology and industry structure adjustment.

Long term electricity load forecasting based on regional load model using optimization techniques: A case study

ABSTRACT Long-term load forecasting is a significant and complex topic in electric distribution systems. Forecasters is need to proper forecasting methodologies and smart solutions to minimize complexity. In this study, regional long-term load forecasting is presented, for Sivas province of Turkey, taking into account the development plan of the municipality, and subscriber profiles. Firstly, the municipality development plan is divided into regions of similar load characteristics. The load demand values of each region are defined mathematically using the S curve. The optimal parameter values of the S curve are calculated using meta-heuristic methods such as Genetic Algorithm (GA), Grey Wolf Optimization (GWO) and Harris Hawk Optimization (HHO). The obtained results are compared with the results of the econometrics-based (top-to-bottom) approach and actual consumer projection. The consumption values between 2004 and 2014 are used for parameter estimation of S curves. The consumption values obtained as the result of analysis the period between 2015 and 2018 were selected as test data. The result is shown that S curve-based regional demand forecast demonstrated more convenient results using the HHO algorithm with statistical values of RE = 1.3362, MAE = 1.5145, RMSE = 1.80385 and STD = 2.122 can be applied to the forecast regional electricity consumption. The proposed method is simple and can be easily applied to forecast the total consumption of the power load for a province any load forecasting region. The presented approach can be used to define the future projections of electricity distribution systems and determine the correct investment strategies.

Regional electricity consumption mixes using trade data for representative inventories

Electricity flows are frequently used life cycle assessment modeling and are often a significant source of emissions so creating inventories based on detailed inventories, representative of regional differences in grid supplied power, is important. We sought to understand the influence electricity trades have on consumption mixes and developed a method for creating regionalized US electricity inventories based on publicly available data for electricity generation and trades. With generation inventories for the eGRID subregions and trading within NERC regions, the trading operates within boundaries representative of distinct production regions while enabling trading. We created two approaches for allocating electricity trades within NERC regions, gross, and net trading. Trading is modeled using FERC data. Gross trading assumes that all trades within a NERC region contribute to each eGRID subregion receiving trades in to satisfy their demand while the net trading approach assumes that only eGRID subregions producing surplus electricity contribute to the mix in regions with deficits. We introduce the concept of trade pools for modeling electricity flows between individual eGRID subregions and the corresponding NERC regions. Challenges for modeling electricity trade flows are described in the Methodology section. We compare the results of the two trading approaches demonstrating the influence of prioritizing eGRID subregion generation mixes or trading within NERC regions. While trades represent a relatively small portion of total consumed electricity, some eGRID regions, like CAMX and those in New York state, rely heavily on trade to in order to meet their consumptive demand. The method presented here establishes the basis for eGRID-level consumption mixes which account for energy trades and provide regionalized life cycle inventories for electricity in the USA. We provide recommendations for their use including in attributional LCA as well as approaches requiring marginal electricity demands. The trading approaches have been implemented in a US EPA repository to provide these inventories and can be found at https://github.com/USEPA/ElectricityLCI .

Short-Term Electricity Consumption Forecasting Based on the EMD-Fbprophet-LSTM Method

Short-term electricity consumption data reflects the operating efficiency of grid companies, and accurate forecasting of electricity consumption helps to achieve refined electricity consumption planning and improve transmission and distribution transportation efficiency. In view of the fact that the power consumption data is nonstationary, nonlinear, and greatly influenced by the season, holidays, and other factors, this paper adopts a time-series prediction model based on the EMD-Fbprophet-LSTM method to make short-term power consumption prediction for an enterprise's daily power consumption data. The EMD model was used to decompose the time series into a multisong intrinsic mode function (IMF) and a residual component, and then the Fbprophet method was used to predict the IMF component. The LSTM model is used to predict the short-term electricity consumption, and finally the prediction value of the combined model is measured based on the weights of the single Fbprophet and LSTM models. Compared with the single time-series prediction model, the time-series prediction model based on the EMD-Fbprophet-LSTM method has higher prediction accuracy and can effectively improve the accuracy of short-term regional electricity consumption prediction.