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

被引:5
|
作者
Al-Zadjali, Saira [1 ]
Al Maashri, Ahmed [1 ]
Al-Hinai, Amer [1 ,2 ]
Al-Yahyai, Sultan [3 ]
Bakhtvar, Mostafa [1 ]
机构
[1] Sultan Qaboos Univ, Elect & Comp Engn, Al Khoud 123, Oman
[2] Sultan Qaboos Univ, Sustainable Energy Res Ctr, Al Khoud 123, Oman
[3] Mazoon Elect Co, Informat & Technol, Fanja 600, Oman
关键词
renewable energy; wind speed nowcasting; ensemble artificial neural networks; MODEL; OAXACA;
D O I
10.3390/en12224355
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
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.
引用
收藏
页数:20
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