Data-Driven Forecasting of Solar PV Output Using Machine Learning: A Comprehensive Approach for Long-Term Prediction

This paper examines the details and selects a machine-learning algorithm for predicting the long-term output of solar photovoltaic (PV) plants. Several algorithms were therefore tested in real-time with ten-minute data obtained for two years. To generate the results, a model was fed, trained, and validated with positive and negative real power and time parameters. In the test phase, models are trained and fitted. The one with the most accurate ability to forecast the target variable is compared against the present values (anticipated output values) to validate the forecast. Based on the statistical assessments, the algorithm’s performance is also evaluated. The output resulted in assumptions about the PV plant’s production. Based on the information from those assumptions, the necessary decisions are made. Random forest regression provides superior accuracy in the long-term forecasting of solar output in a plant than other models. Such findings would be useful to solar engineers and grid operators in the solar energy sector. Solar engineers and grid operators in the solar energy sector would benefit from these findings.