Binding statistical and machine learning models for short-term forecasting of global solar radiation

Authors:
Llanos Mora-López;Ildefonso Martínez-Marchena;Michel Piliougine;Mariano Sidrach-de-Cardona
Affiliations:
Dpto. Lenguajes y C.Computacion, ETSI Informática;Dpto. Lenguajes y C.Computacion, ETSI Informática;Dpto Física Aplicada II, E.Politécnica Superior, University of Malaga, Málaga, Spain;Dpto Física Aplicada II, E.Politécnica Superior, University of Malaga, Málaga, Spain
Venue:
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Year:
2011

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Abstract

A model for short-term forecasting of continuous time series has been developed. This model binds the use of both statistical and machine learning methods for short-time forecasting of continuous time series of solar radiation. The prediction of this variable is needed for the integration of photovoltaic systems in conventional power grids. The proposed model allows us to manage not only the information in the time series, but also other important information supplied by experts. In a first stage, we propose the use of statistical models to obtain useful information about the significant information for a continuous time series and then we use this information, together with machine learning models, statistical models and expert knowledge, for short-term forecasting of continuous time series. The results obtained when the model is used for solar radiation series show its usefulness.