Details

Autor(en) / Beteiligte

• Paiva, Gabriel Mendonca de
• Pimentel, Sergio Pires
• Marra, Enes Goncalves
• Alvarenga, Bernardo Pinheiro de
• Mussetta, Marco
• Leva, Sonia

Titel

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

Ist Teil von

• 2019 IEEE Milan PowerTech, 2019, p.1-5

Ort / Verlag

IEEE

Erscheinungsjahr

2019

Link zum Volltext

Quelle

IEEE Xplore

Beschreibungen/Notizen

• 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.