Evaluation and Prediction of Wind Power Utilization Efficiency Based on Super-SBM and LSTM Models: A Case Study of 30 Provinces in China

Complexity 2020:1-13 (2020)
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Although China’s wind industry has made great progress in recent years, the wind abandonment phenomenon caused by the unbalanced development of regional wind power is still prominent. It is particularly important for the scientific development of wind power to accurately measure the utilization efficiency of wind power and understand its regional differences in China. This study establishes the improved super-efficiency slack-based measure model and long short-term memory network models, systematically and comprehensively measures and predicts the wind power utilization efficiency of 30 regions in China from 2013 to 2020, and explores regional differences in wind power utilization efficiency. Our results show the following: China’s overall wind power utilization efficiency is relatively low but has been on a steady upward trend since 2013. Regional differences are obvious, showing that the spatial distribution pattern of wind power utilization efficiency is greatest in Northeast China, followed by North China, East China, South China, Northwest China, and Central China. The “Three-North” region with abundant wind energy resources has relatively high wind power utilization efficiency and exhibits a good development trend. East China, South China, and Central China, where wind energy resources are relatively poor, have low wind power utilization efficiency, and their development trends are not stable and are more prone to change. The utilization efficiency of wind power in coastal areas is generally better than that in inland areas. There are also differences among the thirty Chinese regions studied. Inner Mongolia and Shandong have achieved real efficiency in wind power utilization efficiency, with optimal allocation of input and output, and a good development trend. The other 28 regions have varying degrees of inefficiency, and there is still room for improvement.



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Peng Zhou
University of Bologna

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