Prediction of daily photovoltaic plant energy output using machine learning

The energy crisis and global warming are the two significant factors that have led to the accelerated development of PV power plants. Accurate prediction of PV plant power output is crucial to enhance its competitiveness in the renewable energy market, maintain grid stability, and reduce reliance on conventional energy sources. The present study uses nine machine-learning methods to estimate PV plant energy output. The usefulness of the examined models for real-time energy output prediction is assessed to guarantee optimum security and management in the renewable energy sector. The datasets used in this study represent data from 01 Jan 2019 to 30 Nov 2022 and pertain to the solar PV plant located in Bhopal city. The specific application of these methods to the actual plant dataset adds practical relevance, providing insight into their feasibility in a real-world situation. The results were compared using five statistical metrics (MAE, MSE, RMSE, R2, and MAPE) to demonstrate the study’s quality and reproducibility. The results show that MAE, MSE, RMSE, R2, and MAPE values of all algorithms range from 19.7 kWh to 76.6 kWh, from 5408.0 kWh to 14,958.0 kWh, from 73.5 kWh to 122.3 kWh, from 86.5% to 98.4%, from 3.7% to 12.9% respectively. The ANN and the RF models demonstrated superior accuracy compared to other models, with ANN being the top-performing model.