Short-Term Power Forecasting Model for Photovoltaic Plants Based on Historical Similarity

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

Autores

Cláudio Monteiro (Autor) FEUP; Santos, T (Autor) Outra; Alfredo Fernandez Jimenez, LA (Autor) Outra; Ramirez Rosado, IJ (Autor) Outra; Sonia Terreros Olarte, MS (Autor) Outra

Revista

Título: Energies

Vol. 6

Páginas: 2624-2643

ISSN: 1996-1073

Editora: MDPI

Indexação

ISI Web of Knowledge - 80 Citações

Scopus - 92 Citações

Outras Informações

ID Authenticus: P-005-0VZ

DOI: 10.3390/en6052624

Abstract (EN): This paper proposes a new model for short-term forecasting of electric energy production in a photovoltaic (PV) plant. The model is called HIstorical SImilar MIning (HISIMI) model; its final structure is optimized by using a genetic algorithm, based on data mining techniques applied to historical cases composed by past forecasted values of weather variables, obtained from numerical tools for weather prediction, and by past production of electric power in a PV plant. The HISIMI model is able to supply spot values of power forecasts, and also the uncertainty, or probabilities, associated with those spot values, providing new useful information to users with respect to traditional forecasting models for PV plants. Such probabilities enable analysis and evaluation of risk associated with those spot forecasts, for example, in offers of energy sale for electricity markets. The results of spot forecasting of an illustrative example obtained with the HISIMI model for a real-life grid-connected PV plant, which shows high intra-hour variability of its actual power output, with forecasting horizons covering the following day, have improved those obtained with other two power spot forecasting models, which are a persistence model and an artificial neural network model.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 20