A Novel Prediction Approach for Short-Term Renewable Energy Consumption in China Based on Improved Gaussian Process Regression

Yuansheng Huang, Lei Yang, Chong Gao, Yuqing Jiang and Yulin Dong
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Yuansheng Huang: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Lei Yang: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Chong Gao: School of Economics and Management, North China Electric Power University, Baoding 071003, China
Yuqing Jiang: School of Economics and Management, North China Electric Power University, Baoding 071003, China
Yulin Dong: School of Economics and Management, North China Electric Power University, Baoding 071003, China

Energies, 2019, vol. 12, issue 21, 1-17

Abstract: Energy consumption issues are important factors concerning the achievement of sustainable social development and also have a significant impact on energy security, particularly for China whose energy structure is experiencing a transformation. Construction of an accurate and reliable prediction model for the volatility changes in energy consumption can provide valuable reference information for policy makers of the government and for the energy industry. In view of this, a novel improved model is developed in this article by integrating the modified state transition algorithm (MSTA) with the Gaussian processes regression (GPR) approach for non-fossil energy consumption predictions for China at the end of the 13th Five-Year Project, in which the MSTA is utilized for effective optimization of hyper-parameters in GPR. Aiming for validating the superiority of MSTA, several comparisons are conducted on two well-known functions and the optimization results show the effectiveness of modification in the state transition algorithm (STA). Then, based on the latest statistical renewable energy consumption data, the MSTA-GPR model is utilized to generate consumption predictions for overall renewable energy and each single renewable energy source, including hydropower, wind, solar, geothermal, biomass and other energies, respectively. The forecasting results reveal that the proposed improved GPR can promote the forecasting ability of basic GPR and obtain the best prediction effect among all the other comparison models. Finally, combined with the forecasting results, the trend of each renewable energy source is analyzed.

Keywords: renewable energy consumption; Gaussian processes regression; state transition algorithm; five-year project; forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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