Distributed Photovoltaic Power Forecast Methods: A Review

Climate change and global warming threaten both nature and humanity. Renewable energy is an effective way to solve this problem. Distributed photovoltaics (DPV) have attracted much attention due to their low environmental impact. But the uncertainty of DPV output is putting pressure on the distribution network. Therefore, DPV power prediction is very important. In this paper, the factors affecting DPV yield are reviewed and the forecasting methods are summarized. The results show that geographical location, weather parameters, photovoltaic panel characteristics, and data noise will affect the prediction results. The main forecasting methods are statistical forecasting and principle forecasting. Statistical forecasting methods perform better in forecasting. In the future, prediction algorithms will need to be improved to adapt to different conditions, such as weather and time scales. Data preprocessing algorithms also help to improve the accuracy and stability of predictions.