Intra-day forecasting of building-integrated PV systems for power systems operation using ANN ensemble

Renewable energy sources (RES) like solar and wind, naturally present high daily and seasonal variability of power generation. This paper presents a real case study of solar photovoltaic (PV) power forecasting of a building-integrated PV plant as a tool for power systems operation. A new methodology composed of data processing strategy, Artificial Neural Networks (ANN) modelling, and error metrics definitions are proposed and validated for the intra-day PV power forecasting. Data were modelled in time steps of 15-minute averages and the forecasts obtained from 15 minutes to 120 minutes ahead. A persistence model is developed for the building-integrated PV systems (BIPVS) case. The forecast skills achieved range from 9.79% to 23.75%, which measures the ANN improvement in relation to the proposed benchmark persistence model.