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Combination Forecasting Method of Short-term Photovoltaic Power Based on Weather Classification
Author:
Affiliation:

College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China

Abstract:

The fluctuation characteristics of the photovoltaic (PV) power are closely related to the weather types, and the short-term PV power forecasting has problems of low forecasting accuracy in the power fluctuation process and the weak correlation between meteorological factors and the power fluctuation process. This paper proposes a combination forecasting method of short-term PV power based on weather classification. Firstly, the weather process is divided into five types based on the meteorological factors and fluctuation characteristics of PV power. Based on the variational mode decomposition algorithm, the PV power is decomposed into the clear-sky-like process and the fluctuation process. Secondly, the Granger causality algorithm is used to select the key meteorological factors, which are closely related to the fluctuation process of PV power with various weather types. Finally, a combined forecasting model of short-term PV power based on weather classification is established. The model fully considers the specificity of the deep learning algorithm, separately forecasts the clear-sky-like process and the fluctuation process of PV power with various weather types. The simulation results show that the proposed short-term PV power forecasting method can significantly improve the accuracy of the short-term PV power forecasting.

Keywords:

Foundation:

This work is supported by National Key R&D Program of China (No. 2018YFB0904200) and State Grid Corporation of China (No. SGLNDKOOKJJS1800266).

Get Citation
[1]YE Lin, PEI Ming, LU Peng, et al. Combination Forecasting Method of Short-term Photovoltaic Power Based on Weather Classification[J]. Automation of Electric Power Systems,2021,45(1):44-54. DOI:10.7500/AEPS20200613003
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History
  • Received:June 13,2020
  • Revised:October 10,2020
  • Adopted:
  • Online: January 05,2021
  • Published: