Photovoltaic Power Forecasting Based on GMM Weather Classification and BiLSTM-ATTENTION

The high integration of photovoltaic (PV) systems introduces significant grid instability and randomness to distribution networks. Understanding PV power output is crucial for improving grid stability. To address this, this paper proposes a hybrid PV power forecasting model based on GMM (Gaussian Mixture Model) weather classification. Firstly, PV generation data is normalized and analyzed for correlations. The GMM clustering algorithm is then used to classify the data into three weather types: sunny, rainy, and cloudy. Next, a BiLSTM-Attention neural network is constructed by integrating BiLSTM (Bidirectional Long Short-Term Memory) with an attention mechanism to enhance prediction. Finally, case study comparisons demonstrate that the proposed model can effectively capture local features with high prediction variability under different weather conditions, significantly improving both the accuracy and stability of PV power forecasting.