A Cascaded Data-Driven Approach for Photovoltaic Power Output Forecasting

Accurate photovoltaic (PV) power output forecasting is critical for ensuring stable operation of modern power systems, yet it is constrained by high-dimensional redundancy in input weather data and the inherent heterogeneity of output scenarios. To address these challenges, this paper proposes a novel cascaded data-driven forecasting approach that enhances forecasting accuracy through systematically improving and optimizing the feature extraction, scenario clustering, and temporal modeling. Firstly, guided by weather data–PV power output correlations, the Deep Autoencoder (DAE) is enhanced by integrating Pearson Correlation Coefficient loss, reconstruction loss, and Kullback–Leibler divergence sparsity penalty into a multi-objective loss function to extract key weather factors. Secondly, the Fuzzy C-Means (FCM) algorithm is comprehensively refined through Mahalanobis distance-based sample similarity measurement, max–min dissimilarity principle for initial center selection, and Partition Entropy Index-driven optimal cluster determination to effectively cluster complex PV power output scenarios. Thirdly, a Long Short-Term Memory–Temporal Pattern Attention (LSTM–TPA) model is constructed. It utilizes the gating mechanism and TPA to capture time-dependent relationships between key weather factors and PV power output within each scenario, thereby heightening the sensitivity to key weather dynamics. Validation using actual data from distributed PV power plants demonstrates that: (1) The enhanced DAE eliminates redundant data while strengthening feature representation, thereby enabling extraction of key weather factors. (2) The enhanced FCM achieves marked improvements in both the Silhouette Coefficient and Calinski–Harabasz Index, consequently generating distinct typical output scenarios. (3) The constructed LSTM–TPA model adaptively adjusts the forecasting weights and obtains superior capability in capturing fine-grained temporal features. The proposed approach significantly outperforms conventional approaches (CNN–LSTM, ARIMA–LSTM), exhibiting the highest forecasting accuracy (97.986%), optimal evaluation metrics (such as Mean Absolute Error, etc.), and exceptional generalization capability. This novel cascaded data-driven model has achieved a comprehensive improvement in the accuracy and robustness of PV power output forecasting through step-by-step collaborative optimization.