Optimizing Forecast Accuracy in Photovoltaic System with Hybrid Artificial Intelligence Model

Photovoltaic (PV) power generation exhibits considerable sensitivity to both weather variability and fluctuations in solar irradiance. Consequently, precise forecasting of PV power is crucial for ensuring grid reliability, load balancing, and the effective functioning of energy markets within a grid-connected solar plant. Conventional forecasting methodologies frequently prove inadequate in accurately capturing the nonlinear and intricate temporal patterns present within PV datasets. To address these shortcomings, this research presents a hybrid short-term PV power forecasting model. This model integrates Neighborhood Component Analysis (NCA) for dimensionality reduction with a Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) framework. NCA helps reduce computational complexity while still preserving the important features of high-dimensional PV data. CNN layers are designed to extract spatial features that are localized. In contrast, the LSTM component is adept at capturing temporal dependencies. This combination allows the model to effectively process short-term dynamics. The hybrid model under consideration was evaluated using empirical data obtained from a solar power plant, and its performance was compared to that of conventional machine learning models and individual deep learning (DL) models. The evaluation of performance revealed a MAE of 0.1945, a MAPE of 7.1118, a RMSE of 0.3645, and an R2 value of 0.976, thereby confirming its enhanced predictive abilities. These results indicate that the hybrid model improves accuracy, precision, and stability in the forecasting of PV power. Consequently, this enhancement underscores the potential of a hybrid DL model to optimize real-time energy management within renewable power systems.