Prediction of Photovoltaic Output Power using Hybrid Artificial Neural Network

With the increasing adoption of rooftop photovoltaic (PV) systems, accurate power output forecasting has become essential for effective energy management and grid integration. This study proposes a hybrid Artificial Neural Network (ANN) model optimized using the Salp Swarm Algorithm (SSA) to enhance prediction accuracy for rooftop PV output. SSA was selected for its strong exploration and exploitation capabilities, which complement the ANN’s learning strengths. Historical PV data from two university campuses in Malaysia, representing varied climatic conditions, were used over a one-year period to ensure model robustness. Key input variables influencing PV output were identified through correlation analysis, enabling more focused ANN training. SSA was used to optimize the ANN’s initial weights and biases, accelerating convergence and improving accuracy. Across three test cases, the SSA-ANN model achieved Mean Squared Error (MSE) values as low as 0.0155 and correlation coefficients (R) up to 0.98069, significantly outperforming standalone ANN approaches. These results demonstrate the model's effectiveness in improving PV forecasting accuracy, offering practical benefits for urban energy planning and sustainable power systems.