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Energies 2016, 9(3), 168; doi:10.3390/en9030168

Improved Spatio-Temporal Linear Models for Very Short-Term Wind Speed Forecasting

Department of Electrical and Electronics Engineering, Anadolu University, Eskisehir 26555, Turkey
Academic Editor: Simon J. Watson
Received: 5 January 2016 / Revised: 15 February 2016 / Accepted: 25 February 2016 / Published: 7 March 2016
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Abstract

In this paper, the spatio-temporal (multi-channel) linear models, which use temporal and the neighbouring wind speed measurements around the target location, for the best short-term wind speed forecasting are investigated. Multi-channel autoregressive moving average (MARMA) models are formulated in matrix form and efficient linear prediction coefficient estimation techniques are first used and revised. It is shown in detail how to apply these MARMA models to the spatially distributed wind speed measurements. The proposed MARMA models are tested using real wind speed measurements which are collected from the five stations around Canakkale region of Turkey. According to the test results, considerable improvements are observed over the well known persistence, autoregressive (AR) and multi-channel/vector autoregressive (VAR) models. It is also shown that the model can predict wind speed very fast (in milliseconds) which is suitable for the immediate short-term forecasting. View Full-Text
Keywords: wind energy; wind speed; very short-term; forecasting; prediction; spatio-temporal; multi-channel; autoregressive moving average model wind energy; wind speed; very short-term; forecasting; prediction; spatio-temporal; multi-channel; autoregressive moving average model
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Filik, T. Improved Spatio-Temporal Linear Models for Very Short-Term Wind Speed Forecasting. Energies 2016, 9, 168.

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