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

• Type: Article in International Scientific Journal
• Date: 2013

Authors

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

Journal

Title: Mathematical Problems in Engineering

Vol. 2013

Pages: 1-9

ISSN: 1024-123X

Publisher: Hindawi

Indexing

ISI Web of Knowledge - 59 Citations

Scopus - 73 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 9