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Very short-term probabilistic wind power forecasting based on Markov chain models

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4 Author(s)
Carpinone, A. ; Inf. Eng. Dept., Second Univ. of Naples, Aversa, Italy ; Langella, R. ; Testa, A. ; Giorgio, M.

Wind power forecasting methods generally provide estimates of future wind power as point forecasts, but most of the decision making processes in electrical power systems management require more information than a single value. For this purpose, additional methods - complex or based on strong assumptions - have been developed for estimating so-called interval forecasts associated to point forecasts. The method proposed by the authors is based on the use of discrete time Markov chain models of a proper order, developed starting from wind power time series analysis. It allows to directly obtain in an easy way an estimate of the wind power distributions on a very short-term horizon, without requiring restrictive assumptions on wind power probability distribution. With reference to an application, results obtained via a First and Second Order Markov Chain Model, respectively, are compared to those of Persistent Model evaluating the related prediction errors.

Published in:

Probabilistic Methods Applied to Power Systems (PMAPS), 2010 IEEE 11th International Conference on

Date of Conference:

14-17 June 2010