Electricity Consumption In China Research Articles

Multi-strategy Hybrid Coati Optimizer: A Case Study of Prediction of Average Daily Electricity Consumption in China

Multi-strategy Hybrid Coati Optimizer: A Case Study of Prediction of Average Daily Electricity Consumption 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.

Forecasting electricity consumption in China's Pearl River Delta urban agglomeration under the optimal economic growth path with low-carbon goals: Based on data of NPP-VIIRS-like nighttime light

To reflect the trend of electricity consumption (EC) in China's Pearl River Delta (PRD) under a balanced environment and economy, this paper proposes an EC forecasting framework from the perspective of optimal economic growth. The historical GDP statistics of the PRD are calibrated with data from the new "NPP-VIIRS-like" nighttime light dataset from 2000 to 2020, and two simulation scenarios of optimal economic growth are constructed according to the carbon emission reduction rate. Finally, based on the electricity data from 2000 to 2017, an error correction model is constructed to predict and compare the trend of EC in the PRD under different carbon emission scenarios. The results demonstrate the following. (1) The economic growth path under low-carbon constraints is more closely aligned with the actual economic development of the PRD. (2) The electricity demand required to sustain optimal economic growth in the PRD under low-carbon constraints is projected to reach saturation around 2037, approximately a decade earlier than the scenario without carbon constraints. The results of the projections are expected to guide future work on power system planning and economic development assessment in the context of carbon reduction.

Projection of Non-Industrial Electricity Consumption in China’s Pearl River Delta under Global Warming Scenarios

Global warming is a global issue closely linked to sustainability, and power systems around the world are facing immense pressure due to global warming. The purpose of this study is to investigate the impact of global warming on non-industrial electricity consumption in China’s Pearl River Delta. The Weather Research and Forecasting (WRF) model is employed to dynamically downscale and simulate summer climate change characteristics during historical periods and future warming scenarios of 1.5/2 °C. Then, in order to dynamically investigate the changes in non-industrial electricity consumption in cities after warming, we developed a non-industrial electricity consumption estimation model based on degree days and GDP. The regression model can well reproduce non-industrial electricity consumption in summer. Under future warming scenarios of 1.5/2 °C, the results indicate an annual growth trend in non-industrial electricity consumption due to global warming. Under a 1.5 °C warming scenario, non-industrial electricity consumption in both Guangzhou and Zhuhai increases, with Guangzhou experiencing a larger increase of about 10 terawatt-hours (TWh) compared to the historical period. However, under a 2 °C warming scenario, non-industrial electricity consumption in both cities slightly decreases compared to the 1.5 °C warming scenario, with a maximum decrease of 874 million kilowatt-hours.

Consumer reaction to green subsidy phase-out in China: Evidence from the household photovoltaic industry

With the impending post-subsidy era, the Chinese government has initiated significant reductions in household photovoltaic (PV) subsidies. This policy change may have negative implications, such as the emergence of the “solar rush” phenomenon. This study aims to quantify the impact of the phase-out of photovoltaic generation subsidies on household electricity consumption in China. We collected electricity usage data from 3620 Chinese households, and our results indicate that the announcement of subsidy phase-out led to a larger rebound effect on total electricity consumption. Following the implementation of the subsidy phase-out, this effect gradually weakened, leading to a decrease in grid purchases. This finding suggests that households adjusted their electricity usage patterns to maximize revenue from solar electricity. In the long term, households are more inclined to generate revenue by selling solar electricity to grid companies during the daytime. This shift in behavior is expected to reshape their electricity usage habits over time. Our findings contribute new insights to the growing body of literature on household consumption and provide practical foundations for the renewable energy market and public policy development.

Regional Heterogeneity Analysis of Residential Electricity Consumption in Chinese Cities: Based on Spatial Measurement Models

The share of electricity consumption by urban and rural residents in China’s overall electricity consumption is very close to that of the tertiary sector, which has become an important driver of China’s electricity consumption growth. At the same time, due to the mismatch between China’s regional resource endowments and the level of regional development, the regional supply and demand situation for electricity varies. Therefore, it is urgent to clarify the regional differences in residential electricity consumption and the factors affecting it, and accordingly adopt targeted and feasible measures to regulate residential electricity consumption. This article includes data from 285 Chinese prefecture-level cities from 2006 to 2019, and adopts a “three lines” method of region-partitioning (Qinling–Huaihe line, Huhuanyong line, and Shanhaiguan line) to divide four regions. We used spatial econometric models to examine residential electricity consumption and its influencing factors in China from the standpoint of regional heterogeneity. The results show that there is significant regional heterogeneity in residential electricity consumption in China, and the difference between the north of the Shanhaiguan line and other areas is significant. Moreover, there is a positive spatial correlation in the per capita domestic electricity consumption of urban residents, and each influencing factor has obvious regional heterogeneity, among which household appliances are the significant influencing factor. Based on the regional heterogeneity of residential electricity consumption, management measures should be formulated according to local conditions, and the supply of electricity should be ensured by strengthening multidimensional initiatives.

Internet usage and household electricity consumption

This paper investigates the impact of the popularization and usage of the Internet on household electricity consumption in China, as well as the mediating role of sleep duration. By employing data from the China Family Panel Studies (CFPS) and employing the basic ordinary least squares (OLS) model, the mediation model, and the instrumental variable (IV) approach, we derive the following conclusions. The results from the basic OLS regression indicate a positive relationship between internet usage and household electricity expenditure, implying that households that use the Internet tend to have higher electricity bills. Subsequently, by introducing sleep duration as a mediating variable, we find that internet usage leads to shorter sleep duration, indirectly resulting in increased household electricity costs. To address potential endogeneity concerns, we employ the instrumental variable approach to correct for the impact of internet usage on household electricity consumption. In addition, through heterogeneity analysis, we found that internet usage impacts households with different characteristics.

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.

A comprehensive model to explain consumers’ purchasing intention of energy-efficient household appliances: A case study in China

ABSTRACT Given the significant increase in residential electricity consumption in China, promoting the use of energy-efficient household appliances in residential buildings is imperative. Considering this, we construct a new comprehensive model, including the theory of planned behavior (TPB) and Theory of value belief norms (VBN), to identify consumers’ intentions to purchase energy-efficient household appliances. The model is empirically tested using a structural equation modeling approach based on 891 effective questionnaires from Chinese respondents. The results show that TPB variables are important factors in predicting consumers’ intentions to purchase energy-efficient household appliances. Product cognition is the best predictor of purchasing intention, followed by perceived behavioral control, environmental awareness, and subjective norms. For VBN variables, the egoistic value and altruistic value placed on the awareness of consequence is insignificant; the other VBN variables all affect purchasing intention. Compared with the original TPB model or VBN model, this comprehensive model exhibits superior predictive ability in the purchase intention of energy-efficient household appliances. In this comprehensive model, environmental awareness creates a significant effect between the awareness of consequence and ascription of responsibility and the interactive influence of subjective norms on the personal norm. Finally, several important policy implications are documented.

Comparative life cycle assessment and techno-economic analysis of electric arc furnace steelmaking processes integrated with solar energy system

Electric arc furnace (EAF) steelmaking process is an environmentally friendly, energy saving, and low-carbon route to produce steel, but its further development is restricted by extensive electricity consumption in China. In this paper, the EAF steelmaking processes driven by solar energy system (EAF-SES) was developed, which supplies electricity for the whole process, and reduce the dependence on traditional energy sources. Life cycle assessment and techno-economic analysis of EAF steelmaking processes integrated with traditional energy system (EAF-TES) and EAF-SES was conducted according to the data of 1536 heats based on the function unit of 1000 kg of molten steel (MS). EAF-SES and EAF-TES were studied and compared from perspectives of life cycle carbon footprint (LCCF), production cost (PC), raw materials cost (RMC), solar energy price (SEP), and carbon tax (CT). The results indicated that EAF-SES enjoyed lower LCCF but higher PC than EAF-TES. The LCCF of EAF-SES was 220 kg lower than that of EAF-TES. Besides, compared to the PC of 478 $ per ton of MS in EAF-TES, the PC in EAF-SES increased by 9.13%. While the electricity price was lower than 0.041 $/kWh or CT higher than 217 $/t CO2, respectively, EAF-SES had both significant advantages of economic and environmental benefits. Vigorously employing clean energy power generation, cutting down hot metal supply, and adopting full scrap melting mode in EAF steelmaking processes achieve better environmental performance. This research is expected to supply a technical idea for energy conservation, emission reduction, and cleaner production in future iron and steel industry.

Research on Influencing Factors of Electricity Consumption in China Based on Grey Theory and Big Data Technology

With the development and application of computer theory, electronic information technology, economics and other disciplines, more and more models are being used to discuss electricity consumption factors. Thesis based on actual data of China, this paper discusses the influence of economic growth, electricity price, urbanization level, industrial structure, science and technology investment level and opening degree on electric power consumption level of our country, and analyses the influence of grey correlation analysis method. To drive electricity consumption, the first is to optimize and upgrade the industrial structure, and the second is to further improve the level of urbanization. The research results of this paper have certain reference value for relevant managers and government departments to formulate policies.

Exploring the drivers and influencing mechanisms of urban household electricity consumption in China - Based on longitudinal data at the provincial level

Exploring the drivers and influencing mechanisms of urban household electricity consumption in China - Based on longitudinal data at the provincial level

How climate change affects electricity consumption in Chinese cities-a differential perspective based on municipal monthly panel data.

Addressing the impacts of climate change has become a global public crisis and challenge. China is characterized by a complex and diverse topography and vast territory, which makes it worthwhile to explore the differential impacts of climate change on urban electricity consumption in different zones and economic development conditions. This study examines the differential impact of climate factors on urban electricity consumption in China based on monthly panel data for 282 prefectures from 2011 to 2019 and projects the potential demand for future urban electricity consumption under different climate change scenarios. The results show that (1) temperature changes significantly alter urban electricity consumption, with cooling degree days (CDD) and heating degree days (HDD) contributing positively to urban electricity consumption in areas with different regional and economic development statuses, with elasticity coefficients of 0.1015-0.1525 and 0.0029-0.0077, respectively. (2) The temperature-electricity relationship curve shows an irregular U-shape. Each additional day of extreme weather above 30 °C and below -12 °C increases urban electricity consumption by 0.52% and 1.52% in the north and by 2.67% and 1.32% in the south. Poor cities are significantly more sensitive to extremely low temperatures than rich cities. (3) Suppose the impacts of climate degradation on urban electricity consumption are not halted. In that case, the possible Shared Socioeconomic Pathways 1-1.9 (SSP1-1.9), SSP1-2.6, and SSP2-4.5 will increase China's urban electricity consumption by 1621.96 billion kWh, 2960.87 billion kWh, and 6145.65 billion kWh, respectively, by 2090. Finally, this study makes some policy recommendations and expectations for follow-up studies.

FORECAST OF RENEWABLE ENERGY GENERATION TREND IN CHINA

Developing and utilizing renewable energy have become a common choice for all countries to ensure energy security, cope with climate change, and achieve sustainable development. Based on the data released in the Statistical Yearbook of China 2021, this paper predicts the data trends of two indicators, installed renewable energy capacity and total electricity consumption, from 2021 to 2030 by using the grey prediction model. According to the forecast results, by 2030, China's renewable energy installed capacity will reach 19,4674 GW, and power generation will be 42,261 billion kW, while the total electricity consumption in China will rise up to 12,738.3 billion kWh. This shows that China will still be unable to achieve its nationwide carbon neutrality goal by 2030. There is still a long way to go to accomplish the whole society's electricity consumption by relying entirely on renewable energy generation, but it has a substantial reference value for China's double carbon target. The recommendations of the research include: continuous increase in the renewable energy installed capacity in the whole society; accelerating energy transformation; strengthening research on renewable energy technology and the environment; establishing a renewable energy data platform; enrichment of the research methods and models for renewable energy development.

Research on the factors influencing the growth of residential electricity consumption

Influenced by the continuous economic and social development and frequent occurrence of extreme weather, the factors influencing the growth of residential domestic electricity consumption in China are increasing, the mechanism of influence is more complex and the trend judgment is more difficult to judge. To this end, this paper studies the influence mechanism of residential domestic electricity consumption growth, researches the driving force of residential domestic electricity consumption growth, carries out correlation and regression analysis of factors influencing residential domestic electricity consumption growth, and draws the following conclusions: the number of air conditioners per 100 households has the strongest influence on per capita residential domestic electricity consumption, followed by the influence of per capita disposable income, and the influence of urbanization rate and population is relatively weak.

Effects of globalization, foreign direct investment and economic growth on renewable electricity consumption

Renewable energy has been seen as a viable solution to the problems of environmental degradation and the energy crisis. This study examines the long – and short–run linkages between economic globalization, foreign direct investment (FDI), economic growth, and renewable electricity consumption in China’s Belt and Road Initiative (BRI) countries. Therefore, this study uses the Pooled Mean Group (PMG) autoregressive distributed lag (ARDL) technique to measure the relationship between constructs based on data collected from 2000 to 2020. The overall results show the collaborative integration of Belt and Road (BRI) countries in terms of globalization, economic growth, and renewable electricity utilization. The results show that there is a long-term positive relationship between FDI and renewable electricity consumption, but a negative relationship in the short term. Furthermore, economic growth is positively correlated with renewable electricity consumption in the long run and negatively correlated in the short run. This study suggests that the governments of BRI countries should encourage globalization by improving technology and knowledge related to renewable electricity consumption in all areas.

Digitalization, Electricity Consumption and Carbon Emissions-Evidence from Manufacturing Industries in China.

The development of China's manufacturing industry is constrained by factors such as energy and resources, and low-carbon development is arduous. Digitalization is an important method to transform and upgrade traditional industries. Based on the panel data of 13 manufacturing industries in China from 2007 to 2019, a regression model and a threshold model were used to empirically test the impact of digitalization and electricity consumption on carbon emissions. The research results were as follows: (1) The digitalization level of China's manufacturing industry was steadily increasing; (2) The proportion of electricity consumption in China's manufacturing industries in the total electricity consumption hardly changed from 2007 to 2019, basically maintaining at about 6.8%. The total power consumption increased by about 2.1 times. (3) From 2007 to 2019, the total carbon emissions of China's manufacturing industry increased, but the carbon emissions of some manufacturing industries decreased. (4) There was an inverted U-shaped relationship between digitalization and carbon emissions, the higher the level of digitalization input, the greater the carbon emissions of the manufacturing industry. However, when digitalization develops to a certain extent, it will also suppress carbon emissions to a certain extent. (5) There was a significant positive correlation between electricity consumption and carbon emissions in the manufacturing industry. (6) There were double energy thresholds for the impact of labor-intensive and technology-intensive manufacturing digitalization on carbon emissions, but only a single economic threshold and scale threshold. There was a single scale threshold for capital-intensive manufacturing, and the value was -0.5352. This research provides possible countermeasures and policy recommendations for digitalization to empower the low-carbon development of China's manufacturing industry.

Forecasting Chinese Electricity Consumption Based on Grey Seasonal Model with New Information Priority

The total electricity consumption in China includes almost all the electricity consumption from all fields, which can reflect the overall situation of China’s electricity consumption, and it is of great significance to forecast it. This paper develops a novel grey Holt-Winters model based on the new information priority cycle accumulation operator, known as the NCGHW model for short, in order to effectively forecast the total electricity consumption in China. First of all, this paper proposes the new information priority cycle accumulation operator to mine the internal law of data while maintaining periodicity in the accumulated data. Then, based on the one-order accumulation sequence generated by the new operator, the framework of the Holt-Winters model is used to build a new model. Finally, according to the characteristics of the data itself, the LBFGS algorithm is used to find the most suitable parameters for the model. In order to model and analyze the fine-grained measurement of the total electricity consumption in China, we study the monthly and quarterly data, respectively. The new model and the contrast models are applied to the two sequences for simulation and prediction. The performance of the model is discussed through relevant evaluation criteria. The results show that the new model has sufficient capacity to forecast the monthly and quarterly total electricity consumption. It is the best choice for the total electricity consumption in China.

Research on the Driving Factors of Urban Electricity-Related Carbon Emissions Based on LMDI

The electrification of energy consumption is the key to achieving the targets of carbon peaking and carbon neutrality. In order to study the main driving factors of electricity-related carbon emissions, a data acquisition and cleaning method adapted to the comparative accounting of carbon dioxide emissions from city-level electricity consumption in China is proposed. An extended LMDI model of urban electricity-related carbon emissions is proposed that comprehensively considers key factors such as population, economic output, industrial structure, power consumption intensity, and power consumption structure. Taking Nantong City as an example, the electricity-related carbon emissions from 2010 to 2020 were calculated, and the impact of each factors on the growth of electricity-related carbon emissions was quantitatively analyzed. The main conclusions are as follows: Firstly, the utilization of clean and low-carbon energy can effectively reduce electricity-related carbon emissions; Secondly, the impact mechanisms of various factors on the changes in electricity-related carbon emissions are not consistent in different stages; Thirdly, economic output and population size are the main contributors to the increase of electricity-related carbon emissions, and the power structure is the main contributor to the suppression of electricity-related carbon emissions.

Optimized multivariate grey forecasting model for predicting electricity consumption: A China study

The power industry has significantly contributed to the prosperity of the national economy, and accurate prediction can reflect the development trend of the power system and power market. The short-term electricity consumption of a country exhibits both annual growth certainty and random change uncertainty, which can be suitably considered with the grey forecasting model. Regarding the short-term trends of electricity consumption in China, this study established an optimized multivariate grey forecasting model with variable background values (OGM(1, N) model) to forecast the electricity consumption level in China. The established model could be converted into the GM(1, N) model and different variant models by adjusting the model parameters. With Beijing, Tianjin and Shanghai as examples, the OGM(1, N) model is compared to the GM(1, N) model and its variant model. The excellent prediction results confirm the feasibility of the proposed model. Then, the proposed model is applied to study China’s electricity consumption. The research results indicated that the OGM(1, N) model attains an extraordinarily high precision in the prediction of electricity consumption and can provide a practical reference for accurate electricity consumption prediction.