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.