A hybrid photovoltaic/wind power prediction model based on Time2Vec, WDCNN and BiLSTM

Abstract

Accurate prediction of photovoltaic (PV)/wind power is an effective solution for the grid stability, reasonable dispatching and power supply reliability. Nowadays, various deep learning prediction methods are developed, most of which are recurrent neural network (RNN)-based and convolutional neural network (CNN)-based. To further improve the prediction performance, a novel hybrid method based on deep CNN with wide first-layer kernels (WDCNN) and bidirection long short-term (BiLSTM) is presented in this paper, in which WDCNN is introduced for large receptive field and useful information extraction, and stacked BiLSTM layers is incorporated to extract temporal correlations of past and future datasets. Besides, Time2Vec is adopted for better feature extraction through decomposing the time series data into non-periodic and periodic components. Several ablation and comparison experiments are carried out with a case study in Yongxing Island, China, and the performance metrics including normalized mean absolute error (NMAE), normalized mean square error (NMSE), normalized root mean square error (NRMSE), and mean absolute scaled error (MASE) confirm the effectiveness and superiority of the proposed model. Compared with individual WDCNN and BiLSTM, the performance of the combined WDCNN-BiLSTM are improved by 5.79%, 6.61%, 3.36%, 5.79% and 2.03%, 3.54%, 1.78%, 2.03% for PV prediction, and 20.38%, 29.37%, 15.96%, 20.38% and 39.12%, 59.68%, 36.50%, 39.12% for wind prediction, respectively. The adoption of Time2Vec further improves the prediction performance by 5.96%, 8.10%, 4.14%, 5.96% for PV prediction, and 4.86%, 9.19%, 4.70%, 4.86% for wind prediction, respectively. The proposed model yields most accurate prediction compared with other competing models. The replacement of CNN with WDCNN improves the prediction accuracy by 6.64%, 8.47%, 4.33%, 6.64% for PV prediction, and 30.59%, 42.78%, 24.35%, 30.59% for wind prediction, respectively. Moreover, the proposed model significantly outperforms compared models in prediction with more random fluctuations, which demonstrates the superiority of the proposed model in mining complex relationships.