Enhancing photovoltaic power prediction using a CNN-LSTM-attention hybrid model with Bayesian hyperparameter optimization

Abstract

Improving the accuracy of solar power forecasting is crucial to ensure grid stability, optimize solar power plant operations, and enhance grid dispatch efficiency. Although hybrid neural network models can effectively address the complexities of environmental data and power prediction uncertainties, challenges such as labor-intensive parameter adjustments and complex optimization processes persist. Thus, this study proposed a novel approach for solar power prediction using a hybrid model (CNN-LSTM-attention) that combines a convolutional neural network (CNN), long short- term memory (LSTM), and attention mechanisms. The model incorporates Bayesian optimization to refine the parameters and enhance the prediction accuracy. To prepare high-quality training data, the solar power data were first preprocessed, including feature selection, data cleaning, imputation, and smoothing. The processed data were then used to train a hybrid model based on the CNN-LSTM-attention architecture, followed by hyperparameter optimization employing Bayesian methods. The experimental results indicated that within acceptable model training times, the CNN-LSTM-attention model outperformed the LSTM, GRU, CNN-LSTM, CNN-LSTM with autoencoders, and parallel CNN-LSTM attention models. Furthermore, following Bayesian optimization, the optimized model demonstrated significantly reduced prediction errors during periods of data volatility compared to the original model, as evidenced by MRE evaluations. This highlights the clear advantage of the optimized model in forecasting fluctuating data.