Advancing Photovoltaic Power Generation Predictions Using Artificial Neural Networks

Solar power offers a free and clean alternative to traditional fossil fuels. Still, the efficiency of solar cells remains suboptimal, making the selection of ideal installation conditions crucial for maximizing energy output. Exact photovoltaic (PV) power generation prediction is essential for the smooth operation and control of solar power plants. The energy generation by a solar panel depends on several factors, including the doping level and design of the PV array, solar radiation, atmospheric temperature, humidity, and dust accumulation. These factors are naturally variable, causing fluctuations in solar cell output throughout the day. Hourly averages or specific interval measurements of these parameters enhance the accuracy of PV module output predictions. To address these challenges, we propose an artificial neural network (ANN)-based approach to forecast photovoltaic power generation under varying environmental conditions. Our method leverages the ability of ANNs to handle complex, non-linear relationships and variable input factors to enhance prediction accuracy. By incorporating detailed environmental data and optimizing sampling rates, our approach aims to provide more reliable and precise PV power generation predictions, ultimately contributing to more efficient and sustainable solar energy utilization.