Wind Power Forecasting with Entropy-Based Criteria Algorithms

Author

Bessa, Ricardo ; Miranda, Vladimiro ; Gama, João

Author_Institution

Inst. de Eng. de Sist. e Comput. do Porto, INESC Porto, Porto

fYear

2008

fDate

25-29 May 2008

Firstpage

1

Lastpage

7

Abstract

This paper reports new results in adopting entropy concepts to the training of mappers such as neural networks to perform wind power prediction as a function of wind characteristics (mainly speed and direction) in wind parks connected to a power grid. Renyi´s Entropy is combined with a Parzen Windows estimation of the error pdf to form the basis of three criteria (MEE, MCC and MEEF) under which neural networks are trained. The results are favourably compared with the traditional minimum square error (MSE) criterion. Real case examples for two distinct wind parks are presented.

Keywords

learning (artificial intelligence); load forecasting; power engineering computing; power grids; wind power; entropy-based criteria algorithms; minimum square error criterion; neural networks; power grid; wind power forecasting; wind power prediction; Economic forecasting; Entropy; Load forecasting; Neural networks; Power generation; Power system planning; Wind energy; Wind energy generation; Wind forecasting; Wind speed;

fLanguage

English

Publisher

ieee

Conference_Titel

Probabilistic Methods Applied to Power Systems, 2008. PMAPS '08. Proceedings of the 10th International Conference on

Conference_Location

Rincon

Print_ISBN

978-1-9343-2521-6

Electronic_ISBN

978-1-9343-2540-7

Type

conf

Filename

4912619