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

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ENERGIES, vol.9, no.3, 2016 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 9 Issue: 3
  • Publication Date: 2016
  • Doi Number: 10.3390/en9030168
  • Journal Name: ENERGIES
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Keywords: forecasting, wind energy, spatio-temporal, prediction, autoregressive moving average model, very short-term, multi-channel, wind speed, ARTIFICIAL NEURAL-NETWORK, BLIND IDENTIFICATION, PREDICTION, GENERATION
  • Anadolu University Affiliated: Yes


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.