Advanced Wind Speed Prediction Model Based on a Combination of Weibull Distribution and an Artificial Neural Network

Athraa Ali Kadhem, Noor Izzri Abdul Wahab, Ishak Aris, Jasronita Jasni and Ahmed N. Abdalla
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Athraa Ali Kadhem: Department of Electrical and Electronic Engineering, University Putra Malaysia, Selangor 43400, Malaysia
Noor Izzri Abdul Wahab: Department of Electrical and Electronic Engineering, University Putra Malaysia, Selangor 43400, Malaysia
Ishak Aris: Department of Electrical and Electronic Engineering, University Putra Malaysia, Selangor 43400, Malaysia
Jasronita Jasni: Department of Electrical and Electronic Engineering, University Putra Malaysia, Selangor 43400, Malaysia
Ahmed N. Abdalla: Faculty of Electronics Information Engineering, Huaiyin Institute of Technology, Huai’an 223003, China

Energies, 2017, vol. 10, issue 11, 1-17

Abstract: One of the most crucial prerequisites for effective wind power planning and operation in power systems is precise wind speed forecasting. Highly random fluctuations of wind influenced by the conditions of the atmosphere, weather and terrain result in difficulties of forecasting regardless of whether it is short-term or long-term. The current study has developed a method to model wind speed data predictions with dependence on seasonal wind variations over a particular time frame, usually a year, in the form of a Weibull distribution model with an artificial neural network (ANN). As a result, the essential dependencies between the wind speed and seasonal weather variation are exploited. The proposed model utilizes the ANN to predict the wind speed data, which has similar chronological and seasonal characteristics to the actual wind data. This model was applied to wind speed databases from selected sites in Malaysia, namely Mersing, Kudat, and Kuala Terengganu, to validate the proposed model. The results indicate that the proposed hybrid artificial neural network (HANN) model is capable of depicting the fluctuating wind speed during different seasons of the year at different locations.

Keywords: wind speed forecasting; artificial neural network; Weibull model; Malaysia (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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