Hybrid Forecasting Methodology for Wind Power-Photovoltaic-Concentrating Solar Power Generation Clustered Renewable Energy Systems

Simian Pang, Zixuan Zheng, Fan Luo, Xianyong Xiao and Lanlan Xu
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Simian Pang: Department of Electrical Engineering, College of Electrical Engineering, Sichuan University, Chengdu 610000, China
Zixuan Zheng: Department of Electrical Engineering, College of Electrical Engineering, Sichuan University, Chengdu 610000, China
Fan Luo: State Grid Gansu Electric Power Company, Lanzhou 730000, China
Xianyong Xiao: Department of Electrical Engineering, College of Electrical Engineering, Sichuan University, Chengdu 610000, China
Lanlan Xu: State Grid Gansu Electric Power Company, Lanzhou 730000, China

Sustainability, 2021, vol. 13, issue 12, 1-16

Abstract: Forecasting of large-scale renewable energy clusters composed of wind power generation, photovoltaic and concentrating solar power (CSP) generation encounters complex uncertainties due to spatial scale dispersion and time scale random fluctuation. In response to this, a short-term forecasting method is proposed to improve the hybrid forecasting accuracy of multiple generation types in the same region. It is formed through training the long short-term memory (LSTM) network using spatial panel data. Historical power data and meteorological data for CSP plant, wind farm and photovoltaic (PV) plant are included in the dataset. Based on the data set, the correlation between these three types of power generation is proved by Pearson coefficient, and the feasibility of improving the forecasting ability through the hybrid renewable energy clusters is analyzed. Moreover, cases study indicates that the uncertainty of renewable energy cluster power tends to weaken due to partial controllability of CSP generation. Compared with the traditional prediction method, the hybrid prediction method has better prediction accuracy in the real case of renewable energy cluster in Northwest China.

Keywords: concentrating solar power; hybrid forecast; long short-term memory; renewable energy cluster (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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