Recursive wind speed forecasting based on Hammerstein Auto-Regressive model

Othman Ait Maatallah, Ajit Achuthan, Kerop Janoyan and Pier Marzocca

Applied Energy, 2015, vol. 145, issue C, 197 pages

Abstract: A new Wind Speed Forecasting (WSF) model, suitable for a short term 1–24h forecast horizon, is developed by adapting Hammerstein model to an Autoregressive approach. The model is applied to real data collected for a period of three years (2004–2006) from two different sites. The performance of HAR model is evaluated by comparing its prediction with the classical Autoregressive Integrated Moving Average (ARIMA) model and a multi-layer perceptron Artificial Neural Network (ANN). Results show that the HAR model outperforms both the ARIMA model and ANN model in terms of root mean square error (RMSE), mean absolute error (MAE), and Mean Absolute Percentage Error (MAPE). When compared to the conventional models, the new HAR model can better capture various wind speed characteristics, including asymmetric (non-gaussian) wind speed distribution, non-stationary time series profile, and the chaotic dynamics. The new model is beneficial for various applications in the renewable energy area, particularly for power scheduling.

Keywords: HAR model; ARIMA model; ANN model; Short term forecast; Iterative multi-steps WSF; Pattern identification (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (41)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261915002093
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:145:y:2015:i:c:p:191-197

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2015.02.032

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().