A New Hybrid Forecasting Strategy Applied to Mean Hourly Wind Speed Time Series | Zendy

Stylianos Sp. Pappas | Zendy; D.C. Karamousantas | Zendy; George E. Chatzarakis | Zendy; Christos Sp. Pappas | Zendy

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A New Hybrid Forecasting Strategy Applied to Mean Hourly Wind Speed Time Series

Author(s) -

Stylianos Sp. Pappas,

D.C. Karamousantas,

George E. Chatzarakis,

Christos Sp. Pappas

Publication year - 2014

Publication title -

journal of wind energy

Language(s) - English

Resource type - Journals

eISSN - 2356-7732

pISSN - 2314-6249

DOI - 10.1155/2014/683939

Subject(s) - wind power , wind speed , kalman filter , renewable energy , autoregressive–moving average model , wind power forecasting , computer science , time series , series (stratigraphy) , mathematical optimization , power (physics) , electric power system , autoregressive model , meteorology , engineering , econometrics , mathematics , artificial intelligence , machine learning , electrical engineering , paleontology , physics , quantum mechanics , biology

An alternative electric power source, such as wind power, has to be both reliable and autonomous. An accurate wind speed forecasting method plays the key role in achieving the aforementioned properties and also is a valuable tool in overcoming a variety of economic and technical problems connected to wind power production. The method proposed is based on the reformulation of the problem in the standard state space form and on implementing a bank of Kalman filters (KF), each fitting an ARMA model of different order. The proposed method is to be applied to a greenhouse unit which incorporates an automatized use of renewable energy sources including wind speed power

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