Forecasting seasonal variations in electricity consumption and electricity usage efficiency of industrial sectors using a grey modeling approach

Chen, Hai-Bao; Pei, Ling-Ling; Zhao, Yufeng

doi:10.1016/j.energy.2021.119952

Cited by 43 publications

(14 citation statements)

References 52 publications

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“…The national average abandonment rate for photovoltaic power generation decreased from 2.2% to 2.1%, increasing by 30.11 million kW. A total of 210 million kilowatts and 204 million kilowatts of grid-connected capacity were generated by wind and photovoltaic power, respectively, by the end of 2019 [44].…”

Section: Recent Development In the Renewable Energy Sectormentioning

confidence: 99%

A Comprehensive Analysis of the Power Demand–Supply Situation, Electricity Usage Patterns, and the Recent Development of Renewable Energy in China

et al. 2022

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Renewable energy is now the world’s most reliable and sustainable solution to environmental pollution, the energy crisis, and social sustainability. In order to regulate renewable energies and ensure the sustainable development of renewable energy in China, a regulatory framework is imperative. Electricity demand and supply forecasting have become one of the most important topics for research on sustainable solutions to energy issues. This article analyzes the Chinese electric power industry structure by looking at historical electricity demand and supply data. This study shows how a divergent set of energy policies can facilitate the application of renewable energy to China’s electric power sector. The analysis shows that in 2020, China’s electricity generation and consumption increased by 0.9 and 1.3 percent annually, respectively, producing capacity of power plants increased by 5.6 GW, coal consumption of power supply decreased by 4.3 g/kWh, power generation projects investment increased by 51.6 billion RMB, power grid projects investment decreased by 1.8 billion RMB, and newly installed generation capacity increased by 769 GW year on year (YOY). During the first three quarters of 2021, the generation, consumption and coal consumption of electricity increased respectively by 10.7 and 12.9%, the generating capacity of power plants increased by 9.4 GW, the average coal consumption of power supply decreased by 0.9 g/kWh, power generation projects investment increased by 1.8 billion RMB, while power grid projects investment decreased by 0.3 billion RMB and new generation capacity added around 20.16 GW compared to the same period last year. Furthermore, domestic power consumption grew by 6.0% and 7%, respectively, in 2020–2021. The paper examines China’s current climate, developments, and renewable energy targets between 2020 and 2021. This research aims to identify the main risks associated with China’s renewable energy development and propose a few policy measures for risk management.

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Section: Recent Development In the Renewable Energy Sectormentioning

confidence: 99%

A Comprehensive Analysis of the Power Demand–Supply Situation, Electricity Usage Patterns, and the Recent Development of Renewable Energy in China

et al. 2022

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“…The key areas of Grey System Theory include grey relational analysis, grey generating space, grey forecasting, grey decision making, grey control, and grey mathematics. Grey System Theory has seen application in numerous fields, including agriculture (Tan et al, 2014), supplier selection (Mahmoudi et al, 2021a), economic growth analysis (Huang et al, 2020), health care management (Aydemir & Sahin, 2019;Javed and Liu., 2018), sustainable development (Ikram et al, 2021;Abid et al, 2021), environment (Hao et al, 2006), electromagnetic data processing (Jiang et al, 2017), traffic flow prediction (Xiao et al, 2020), energy and emissions (Chen et al, 2021;Zhu et al, 2019), project management (Sheikh et al, 2019;Javed & Liu, 2019), machine learning (Xie et al, 2021;Ma, 2019), among others. It can be seen that the application of Grey System Theory is widespread and multi-disciplinary.…”

Section: Soil and Water Conservation (T9)mentioning

confidence: 99%

Evaluation of Low-Carbon Sustainable Technologies in Agriculture Sector through Grey Ordinal Priority Approach

Islam

2021

IJGS

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The agriculture sector plays a vital role in the economy, society, and environment, the three dimensions of sustainability. The agriculture sector contributes 12% to 14% of global greenhouse gas (GHG) emissions to the atmosphere, negatively impacting climate change. Using low-carbon and sustainable agricultural technologies can help mitigate climate change and global food security issues. But selecting and prioritizing the best technologies among all alternatives has always been an issue for decision-makers because of various uncertainty related to the agricultural sector. Therefore, the current study intends to identify and prioritize the key low-carbon and sustainable agricultural technologies. The current study makes a pioneering attempt in employing the Grey Ordinal Priority Approach (OPA-G), a modern multi-attribute decision-making technique, for the evaluation of low-carbon and sustainable technologies for the agricultural sector.

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“…Later, the model was improved by introducing a dynamic seasonality operator, and the improved DSGM(1,1) was successfully applied to the quarterly solar power generation forecasting in the United States (Wang et al , 2020). Chen et al (2021) established a seasonal grey model which combined the buffer operators with the DGGM(1,1). Ding et al (2021) established a discrete seasonal grey model with adaptive time-varying parameters.…”

Section: Introductionmentioning

confidence: 99%

Electric supply and demand forecasting using seasonal grey model based on PSO-SVR

Yao

Mao²

2022

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PurposeGiven the effects of natural and social factors, data on both the supply and demand sides of electricity will produce obvious seasonal fluctuations. The purpose of this article is to propose a new dynamic seasonal grey model based on PSO-SVR to forecast the production and consumption of electric energy.Design/methodology/approachIn the model design, firstly, the parameters of the SVR are initially optimized by the PSO algorithm for the estimation of the dynamic seasonal operator. Then, the seasonal fluctuations in the electricity demand data are eliminated using the dynamic seasonal operator. After that, the time series after eliminating of the seasonal fluctuations are used as the training set of the DSGM(1, 1) model, and the corresponding fitted, and predicted values are calculated. Finally, the seasonal reduction is performed to obtain the final prediction results.FindingsThis study found that the electricity supply and demand data have obvious seasonal and nonlinear characteristics. The dynamic seasonal grey model based on PSO-SVR performs significantly better than the comparative model for hourly and monthly data as well as for different time durations, indicating that the model is more accurate and robust in seasonal electricity forecasting.Originality/valueConsidering the seasonal and nonlinear fluctuation characteristics of electricity data. In this paper, a dynamic seasonal grey model based on PSO-SVR is established to predict the consumption and production of electric energy.

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Forecasting seasonal variations in electricity consumption and electricity usage efficiency of industrial sectors using a grey modeling approach

Cited by 43 publications

References 52 publications

A Comprehensive Analysis of the Power Demand–Supply Situation, Electricity Usage Patterns, and the Recent Development of Renewable Energy in China

A Comprehensive Analysis of the Power Demand–Supply Situation, Electricity Usage Patterns, and the Recent Development of Renewable Energy in China

Evaluation of Low-Carbon Sustainable Technologies in Agriculture Sector through Grey Ordinal Priority Approach

Electric supply and demand forecasting using seasonal grey model based on PSO-SVR

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