Short-Term Photovoltaic Power Forecasting Based on a Feature Rise-Dimensional Two-Layer Ensemble Learning Model

Hui Wang, Su Yan, Danyang Ju, Nan Ma, Jun Fang, Song Wang, Haijun Li, Tianyu Zhang, Yipeng Xie and Jun Wang ()
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Hui Wang: School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Su Yan: School of Arts and Sciences, Northeast Agricultural University, Harbin 150038, China
Danyang Ju: Hulunbuir Power Supply Company of State Grid Inner Mongolia East Electric Power Co., Ltd., Hulunbuir 162650, China
Nan Ma: School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Jun Fang: School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Song Wang: School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Haijun Li: School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Tianyu Zhang: School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Yipeng Xie: Liaoyang Power Supply Company of State Grid Liaoning Electric Power Co., Ltd., Liaoyang 111000, China
Jun Wang: School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China

Sustainability, 2023, vol. 15, issue 21, 1-26

Abstract: Photovoltaic (PV) power generation has brought about enormous economic and environmental benefits, promoting sustainable development. However, due to the intermittency and volatility of PV power, the high penetration rate of PV power generation may pose challenges to the planning and operation of power systems. Accurate PV power forecasting is crucial for the safe and stable operation of the power grid. This paper proposes a short-term PV power forecasting method using K-means clustering, ensemble learning (EL), a feature rise-dimensional (FRD) approach, and quantile regression (QR) to improve the accuracy of deterministic and probabilistic forecasting of PV power. The K-means clustering algorithm was used to construct weather categories. The EL method was used to construct a two-layer ensemble learning (TLEL) model based on the eXtreme gradient boosting (XGBoost), random forest (RF), CatBoost, and long short-term memory (LSTM) models. The FRD approach was used to optimize the TLEL model, construct the FRD-XGBoost-LSTM (R-XGBL), FRD-RF-LSTM (R-RFL), and FRD-CatBoost-LSTM (R-CatBL) models, and combine them with the results of the TLEL model using the reciprocal error method, in order to obtain the deterministic forecasting results of the FRD-TLEL model. The QR was used to obtain probability forecasting results with different confidence intervals. The experiments were conducted with data at a time level of 15 min from the Desert Knowledge Australia Solar Center (DKASC) to forecast the PV power of a certain day. Compared to other models, the proposed FRD-TLEL model has the lowest root mean square error (RMSE) and mean absolute percentage error (MAPE) in different seasons and weather types. In probability interval forecasting, the 95%, 75%, and 50% confidence intervals all have good forecasting intervals. The results indicate that the proposed PV power forecasting method exhibits a superior performance in forecasting accuracy compared to other methods.

Keywords: photovoltaic power forecasting; deterministic forecasting; probability interval forecasting; ensemble learning; feature rise-dimensional approach; quantile regression (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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