An Accurate, Light-Weight Wind Speed Predictor for Renewable Energy Management Systems

Saira Al-Zadjali, Ahmed Al Maashri, Amer Al-Hinai, Sultan Al-Yahyai and Mostafa Bakhtvar
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Saira Al-Zadjali: Electrical & Computer Engineering, Sultan Qaboos University, Al Khodh 123, Oman
Ahmed Al Maashri: Electrical & Computer Engineering, Sultan Qaboos University, Al Khodh 123, Oman
Amer Al-Hinai: Electrical & Computer Engineering, Sultan Qaboos University, Al Khodh 123, Oman
Sultan Al-Yahyai: Information and Technology, Mazoon Electricity Company, Fanja 600, Oman
Mostafa Bakhtvar: Electrical & Computer Engineering, Sultan Qaboos University, Al Khodh 123, Oman

Energies, 2019, vol. 12, issue 22, 1-20

Abstract: This paper proposes an approach for accurate wind speed forecasting. While previous works have proposed approaches that have either underperformed in accuracy or were too computationally intensive, the work described in this paper was implemented using a computationally efficient model. This model provides wind speed nowcasting using a combination of perturbed observation ensemble networks and artificial neural networks. The model was validated and evaluated via simulation using data that were measured from wind masts. The simulation results show that the proposed model improved the normalized root mean square error by 20.9% compared to other contending approaches. In terms of prediction interval coverage probability, our proposed model shows a 17.8% improvement, all while using a smaller number of neural networks. Furthermore, the proposed model has an execution time that is one order of magnitude faster than other contenders.

Keywords: renewable energy; wind speed nowcasting; ensemble artificial neural networks (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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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