Forecasting and Uncertainty Analysis of Day-Ahead Photovoltaic Power Based on WT-CNN-BiLSTM-AM-GMM

Bo Gu (), Xi Li, Fengliang Xu, Xiaopeng Yang, Fayi Wang and Pengzhan Wang
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Bo Gu: School of Electrical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450011, China
Xi Li: School of Electrical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450011, China
Fengliang Xu: Xinyang Power Supply Company of State Grid Henan Electric Power Company, Xinyang 464000, China
Xiaopeng Yang: Henan Jiuyu Tenglong Information Engineering Co., Ltd., Zhengzhou 450052, China
Fayi Wang: Xinyang Power Supply Company of State Grid Henan Electric Power Company, Xinyang 464000, China
Pengzhan Wang: Henan Jiuyu Tenglong Information Engineering Co., Ltd., Zhengzhou 450052, China

Sustainability, 2023, vol. 15, issue 8, 1-27

Abstract: Accurate forecasting of photovoltaic (PV) power is of great significance for the safe, stable, and economical operation of power grids. Therefore, a day-ahead photovoltaic power forecasting (PPF) and uncertainty analysis method based on WT-CNN-BiLSTM-AM-GMM is proposed in this paper. Wavelet transform (WT) is used to decompose numerical weather prediction (NWP) data and photovoltaic power data into frequency data with time information, which eliminates the influence of randomness and volatility in the data information on the forecasting accuracy. A convolutional neural network (CNN) is used to deeply mine the seasonal characteristics of the input data and the correlation characteristics between the input data. The bidirectional long short-term memory network (BiLSTM) is used to deeply explore the temporal correlation of the input data series. To reflect the different influences of the input data sequence on the model forecasting accuracy, the weight of the calculated value of the BiLSTM model for each input data is adaptively adjusted using the attention mechanism (AM) algorithm according to the data sequence, which further improves the model forecasting accuracy. To accurately calculate the probability density distribution characteristics of photovoltaic forecasting errors, the Gaussian mixture model (GMM) method was used to calculate the probability density distribution of forecasting errors, and the confidence interval of the day-ahead PPF was calculated. Using a photovoltaic power station as the calculation object, the forecasting results of the WT-CNN-BiLSTM-AM, CNN-BiLSTM, WT-CNN-BiLSTM, long short-term memory network (LSTM), gate recurrent unit (GRU), and PSO-BP models were compared and analyzed. The calculation results show that the forecasting accuracy of the WT-CNN-BiLSTM-AM model is higher than that of the other models. The confidence interval coverage calculated from the GMM is greater than the given confidence level.

Keywords: wavelet transform; convolutional neural network; bidirectional long short-term memory network; gaussian mixture model; photovoltaic power forecasting; uncertainty analysis (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 (3)

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