Short-Term Prediction of PV Power Based on Combined Modal Decomposition and NARX-LSTM-LightGBM

Hongbo Gao, Shuang Qiu, Jun Fang, Nan Ma, Jiye Wang, Kun Cheng, Hui Wang, Yidong Zhu, Dawei Hu, Hengyu Liu and Jun Wang ()
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Hongbo Gao: School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Shuang Qiu: 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
Nan Ma: School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Jiye Wang: School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Kun Cheng: School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Hui Wang: School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Yidong Zhu: Electric Power Research Institute, State Grid Liaoning Electric Power Co., Ltd., Shenyang 110001, China
Dawei Hu: Electric Power Research Institute, State Grid Liaoning Electric Power Co., Ltd., Shenyang 110001, China
Hengyu Liu: Electric Power Research Institute, State Grid Liaoning Electric Power Co., Ltd., Shenyang 110001, China
Jun Wang: School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China

Sustainability, 2023, vol. 15, issue 10, 1-22

Abstract: Recently, solar energy has been gaining attention as one of the best promising renewable energy sources. Accurate PV power prediction models can solve the impact on the power system due to the non-linearity and randomness of PV power generation and play a crucial role in the operation and scheduling of power plants. This paper proposes a novel machine learning network framework to predict short-term PV power in a time-series manner. The combination of nonlinear auto-regressive neural networks with exogenous input (NARX), long short term memory (LSTM) neural network, and light gradient boosting machine (LightGBM) prediction model (NARX-LSTM-LightGBM) was constructed based on the combined modal decomposition. Specifically, this paper uses a dataset that includes ambient temperature, irradiance, inverter temperature, module temperature, etc. Firstly, the feature variables with high correlation effects on PV power were selected by Pearson correlation analysis. Furthermore, the PV power is decomposed into a new feature matrix by (EMD), (EEMD) and (CEEMDAN), i.e., the combination decomposition (CD), which deeply explores the intrinsic connection of PV power historical series information and reduces the non-smoothness of PV power. Finally, preliminary PV power prediction values and error correction vector are obtained by NARX prediction. Both are embedded into the NARX-LSTM-LightGBM model pair for PV power prediction, and then the error inverse method is used for weighted optimization to improve the accuracy of the PV power prediction. The experiments were conducted with the measured data from Andre Agassi College, USA, and the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the model under different weather conditions were lower than 1.665 kw, 0.892 kw and 0.211, respectively, which are better than the prediction results of other models and proved the effectiveness of the model.

Keywords: PV power prediction; mode decomposition; NARX; LSTM; LightGBM (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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