Abstract:In this paper, a novel method is proposed and applied to quickly calculate the capacity of energy storage for stand-alone and grid-connected wind energy systems, according to the discrete Fourier transform theory. Based on practical wind resource data and power data, which are derived from the American Wind Energy Technology Center and HOMER software separately, the energy storage capacity of a stand-alone wind energy system is investigated and calculated. Moreover, by applying the practical wind power data from a wind farm in Fujian Province, the energy storage capacity for a grid-connected wind system is discussed in this paper. This method can also be applied to determine the storage capacity of a stand-alone solar energy system with practical photovoltaic power data.

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This paper proposes a new technology of spatial prediction for flood susceptibility. Multiple kernel learning was used to build the flood susceptibility model and predict the flood inundation risk of the Sanhuajian Basin of the Yellow River. Based on the historical flow records of the Huayuankou Site and the MODIS remote sensing images of the study area, the maximum inundation range was extracted by the open water likelihood index method, and the flooded and non-flooded sample sites were selected. Considering the availability of pertinent literatures and data, ten flood conditioning factors were defined as the sample characteristics. The model performance was evaluated in terms of accuracy, F1 score, and AUC. According to the results, multiple kernel learning significantly outperforms the support vector machine adopting single kernel, and NLMKL demonstrates the best comprehensive performance. The flood susceptibility map generated by MODIS remote sensing images and multiple kernel learning, therefore, can provide effective help for researchers and decision makers in flood management.

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