Forecasting the annual household electricity consumption of Chinese residents using the DPSO-BP prediction model
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RESEARCH ARTICLE
Forecasting the annual household electricity consumption of Chinese residents using the DPSO-BP prediction model Lei Wen 1 & Xiaoyu Yuan 1 Received: 3 January 2020 / Accepted: 12 March 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract In recent years, global climate change caused by carbon dioxide emissions has attracted more and more attention. Adjusting the energy mix by predicting energy demands is currently a more effective way to address climate issues and energy supply issues. Based on the panel data from 1999 to 2018 in China, this paper designed a new hybrid prediction model to predict the future electricity consumption of Chinese residents by double improvement of particle swarm optimization. By comparing with the BP neural prediction model without mixing and several BP neural prediction models with other improved and mixed forms, the results show that the BP neural network hybrid prediction model with DPSO-BP is more suitable for forecasting the electricity consumption of Chinese residents. At the same time, the prediction results of the DPSO-BP prediction model show that the annual electricity consumption of Chinese residents will increase from 9685 (100 million kWh) in 2018 to 13,171 (100 million kWh) in 2025 in the next 7 years. The research results provide a reference for future scholars in the design of algorithms and provide suggestions for the government to adjust energy and avoid severe power shortages or surpluses. Keywords Electricity forecasting . Electricity consumption . Intelligent algorithm . Algorithm improvement
Introduction With the rapid development of the global economy, the total annual energy demand of developing countries has continued to grow substantially in recent decades. Take China, one of the typical developing countries, as an example. According to the statistics of China’s national statistical yearbook from 1999 to 2018, China’s GDP increased from 9056.44 billion yuan in
Highlights • A novel hybrid prediction model (DPSO-BP) is designed for forecasting. • The novel improvements are proposed to improve particle swarm optimization. • Some new indicators (HCE, UL) are introduced into the prediction model. • A new comparison content is introduced on the accuracy of prediction models. • The electricity consumption of Chinese residents is predicted from 2019 to 2025. Responsible Editor: Muhammad Shahbaz * Xiaoyu Yuan [email protected] 1
Department of Economics and Management, North China Electric Power University, Baoding, Hebei, China
1999 to 9030.95 billion yuan in 2018, with an average annual growth rate of 11.32%. China’s total energy consumption increased from 1405.69 million tons of standard coal in 1999 to 464 million tons in 2018, with an average annual growth rate of 6.01% (Zeng et al. 2017). However, as energy demand continues to grow and energy prices continue to rise, energy supply issues are increasingly becoming an important factor constraining China’s economic development (Lin et al. 2010). As a secondary energy source
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