SSA-LSTM: Short-Term Photovoltaic Power Prediction Based on Feature Matching

Zhengwei Huang (), Jin Huang and Jintao Min
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Zhengwei Huang: College of Economics & Management, China Three Gorges University, Yichang 443000, China
Jin Huang: College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443000, China
Jintao Min: College of Computer and Information Technology, China Three Gorges University, Yichang 443000, China

Energies, 2022, vol. 15, issue 20, 1-16

Abstract: To reduce the impact of volatility on photovoltaic (PV) power generation forecasting and achieve improved forecasting accuracy, this article provides an in-depth analysis of the characteristics of PV power outputs under typical weather conditions. The trend of PV power generation and the similarity between simultaneous outputs are found, and a hybrid prediction model based on feature matching, singular spectrum analysis (SSA) and a long short-term memory (LSTM) network is proposed. In this paper, correlation analysis is used to verify the trend of PV power generation; the similarity between forecasting days and historical meteorological data is calculated through grey relation analysis; and similar generated PV power levels are searched for phase feature matching. The input time series is decomposed by singular spectrum analysis; the trend component, oscillation component and noise component are extracted; and principal component analysis and reconstruction are carried out on each component. Then, an LSTM network prediction model is established for the reconstructed subsequences, and the external feature input is controlled to compare the obtained prediction results. Finally, the model performance is evaluated through the data of a PV power plant in a certain area. The experimental results prove that the SSA-LSTM model has the best prediction performance.

Keywords: photovoltaic power forecast; grey relation analysis; singular spectrum analysis; long short-term memory network; feature matching (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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