Hybrid Models Based on LSTM and CNN Architecture with Bayesian Optimization for ShortTerm Photovoltaic Power Forecasting

Abstract

The precision and reliability of photovoltaic (PV) power forecasting play a crucial role in commercial PV plants. However, the stochastic and intermittent nature of solar radiation makes prediction difficult. Inspired by this, 4 different deep learning-based hybrid models are proposed to predict short-term PV power generation using long short term memory (LSTM) neural network and convolutional neural network (CNN) based on Bayesian Optimization (BO) in this paper. In addition, this paper explores feature selection using two benchmark models on different feature sets, and finally selects 5 features for prediction. The performances of direct forecasting results for both 1-hour ahead and 24-hour ahead of the above various models are compared on one year of hourly data from a real PV plant in Shandong, China. It is shown that using Bi-directional LSTM (BiLSTM) and CNN-BiLSTM models are more suitable for 1-hour ahead prediction, LSTM-CNN and CNN-BiLSTM models are more suitable for 24-hour ahead prediction. The case study shows that the model with Bayesian optimized optimal weights can reduce the error rate by up to 32.80% compared to the benchmark model and demonstrates the good prediction performance of the proposed approach on commercial PV plants.