Markov chain modeling for very-short-term wind power forecasting

By A. Carpinone, Massimiliano Giorgio, R. Langella, A. Testa. 2015

154 citations recorded by the source corpus.

Abstract A Wind power forecasting method based on the use of discrete time Markov chain models is developed starting from real wind power time series data. 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. First and Second Order Markov Chain Model are analytically described. Finally, the application of the proposed method is illustrated with reference to a set of real data.

Subfields: Electrical and Electronic Engineering, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/be6a821af6f42de2e3695fe2bdbb7728a4e92025

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1. M. Franchini
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Citations in the corpus

• A Probabilistic Short-Term Water Demand Forecasting Model Based on the Markov Chain (2017, methodology): “This information can also be used to obtain a deterministic forecast of future water demand qfor (t + k∆t) at a generic time t + k∆t in the following manner [28].”