Development and evaluation of a hybrid PV energy forecasting model

Abstract

Time-series and machine learning techniques have demonstrated significant efficacy in forecasting photovoltaic (PV) energy for intra-day and intra-hour horizons. By integrating radiation measurement with cloud and satellite imagery, accurate predictions can be made for up to six hours. However, for day-ahead (DA) forecasts, numerical weather prediction (NWP) models are necessary as the atmospheric processes driving changes are not sufficiently related to current conditions to be captured using statistical methods alone. This thesis proposes a hybrid ensemble model that integrates satellite imagery with NWP data for intra-day forecasts. Additionally, it explores the full potential of NWP models for DA forecasting by applying model output statistics techniques. This research offers a comprehensive analysis of both deterministic and probabilistic PV power forecasts with uncertainty associated with these predictions, contributing valuable insights to the field of solar energy forecasting