A novel application of artificial neural network for wind speed estimation

作者：Fang Da; Wang Jianzhou^*

来源：International Journal of Sustainable Energy, 2017, 36(5): 415-429.

DOI：10.1080/14786451.2015.1026906

• Data preprocessing
• Elman neural network
• Generalized regression neural networks
• Intelligent data processing
• Power grid securities
• Technical and economic impacts
• Wind speed estimations
• Wind speed forecasting

Providing accurate multi-steps wind speed estimation models has increasing significance, because of the important technical and economic impacts of wind speed on power grid security and environment benefits. In this study, the combined strategies for wind speed forecasting are proposed based on an intelligent data processing system using artificial neural network (ANN). Generalized regression neural network and Elman neural network are employed to form two hybrid models. The approach employs one of ANN to model the samples achieving data denoising and assimilation and apply the other to predict wind speed using the pre-processed samples. The proposed method is demonstrated in terms of the predicting improvements of the hybrid models compared with single ANN and the typical forecasting method. To give sufficient cases for the study, four observation sites with monthly average wind speed of four given years in Western China were used to test the models. Multiple evaluation methods demonstrated that the proposed method provides a promising alternative technique in monthly average wind speed estimation.

• 出版日期2017
• 单位东南大学