Short-Term Forecasting Models for Photovoltaic Plants: Analytical versus Soft-Computing Techniques

• Tipo: Artigo em Revista Científica Internacional
• Data: 2013

Autores

Cláudio Monteiro (Autor) FEUP; Alfredo Fernandez Jimenez, LA (Autor) Outra; Ramirez Rosado, IJ (Autor) Outra; Munoz Jimenez, A (Autor) Outra; Lara Santillan, PM (Autor) Outra

Revista

Título: Mathematical Problems in Engineering

Vol. 2013

Páginas: 1-9

ISSN: 1024-123X

Editora: Hindawi

Indexação

ISI Web of Knowledge - 59 Citações

Scopus - 73 Citações

Outras Informações

ID Authenticus: P-008-JH8

DOI: 10.1155/2013/767284

Abstract (EN): We present and compare two short-term statistical forecasting models for hourly average electric power production forecasts of photovoltaic (PV) plants: the analytical PV power forecasting model (APVF) and the multiplayer perceptron PV forecasting model (MPVF). Both models use forecasts from numerical weather prediction (NWP) tools at the location of the PV plant as well as the past recorded values of PV hourly electric power production. The APVF model consists of an original modeling for adjusting irradiation data of clear sky by an irradiation attenuation index, combined with a PV power production attenuation index. The MPVF model consists of an artificial neural network based model (selected among a large set of ANN optimized with genetic algorithms, GAs). The two models use forecasts from the same NWP tool as inputs. The APVF and MPVF models have been applied to a real-life case study of a grid-connected PV plant using the same data. Despite the fact that both models are quite different, they achieve very similar results, with forecast horizons covering all the daylight hours of the following day, which give a good perspective of their applicability for PV electric production sale bids to electricity markets.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 9