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It is of great significance for wind power plant to construct an accurate multi-step wind speed prediction model, especially considering its operations and grid integration. By integrating with a data pre-processing measure, a parameter optimization algorithm and error correction strategy, a novel forecasting method for multi-step wind speed in short period is put forward in this article. In the suggested measure, the EEMD (Ensemble Empirical Mode Decomposition) is applied to extract a series of IMFs (intrinsic mode functions) from the initial wind data sequence; the LSTM (Long Short Term Memory) measure is executed as the major forecasting method for each IMF; the GRNN (general regression neural network) is executed as the secondary forecasting method to forecast error sequences for each IMF; and the BSO (Brain Storm Optimization) is employed to optimize the parameter for GRNN during the training process. To verify the validity of the suggested EEMD-LSTM-GRNN-BSO model, eight models were applied on three different wind speed sequences. The calculation outcomes reveal that: (1) the EEMD is able to boost the wind speed prediction capacity and robustness of the LSTM approach effectively; (2) the BSO based parameter optimization method is effective in finding the optimal parameter for GRNN and improving the forecasting performance for the EEMD-LSTM-GRNN model; (3) the error correction method based on the optimized GRNN promotes the forecasting accuracy of the EEMD-LSTM model significantly; and (4) compared with all models involved, the proposed EEMD-LSTM-GRNN-BSO model is proved to have the best performance in predicting the short-term wind speed sequence.

Clean and sustainable hydrogen production is key to the establishment of zero-carbon hydrogen energy system in response to the global warming challenge. This paper is aimed to analyze China's potential of hydrogen production from wind and solar power. To achieve this goal, this paper firstly used the SBM model with undesirable output to measure the efficiency of green hydrogen production at the provincial level. Then, the efficiency of green hydrogen production and the installed capacity of wind and solar power are integrated to construct a comprehensive indicator. Lastly, China's potential of green hydrogen production from both provincial and regional perspectives are simulated by entropy method and the dynamic change of the potential from 2017 to 2030 is also studied. From the results, we can find that: (1) the efficiency of the hydrogen production from wind power is significantly higher than that of the hydrogen production from solar power; (2) the efficiency and potential of green hydrogen production for each province of China in 2030 are improved compared with that in 2017; (3) the potential of green hydrogen production in the Northwest and North China is significantly higher than other regions; (4) there is a certain inconsistency in the development of the supply side and demand side of hydrogen energy, specially for the Southern China and northwest. Finally, based on the results above, some policy implications are provided to facilitate the high-quality development of the hydrogen energy industry. INDEX TERMS potential evaluation, green hydrogen production, Data envelopment analysis, entropy method

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