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• W3039339675 abstract "Abstract In recent years, China has promoted many new energy projects in order to meet the growing demand for electricity. Therefore, China's offshore wind power installed capacity has grown rapidly. China has a long coastline and abundant offshore wind energy resources. Offshore wind power is an important area for the development of renewable energy, which can promote wind power technology advancement and energy structure adjustment. Therefore, conducting effective research and forecast on the cumulative installed capacity of China's offshore wind power will help the government to rationally deploy and reduce the risk of investment in offshore wind power. In order to accurately predict the future prospects of offshore wind power in China, this paper firstly constructed a set of influencing factors and used gray correlation analysis to screen the main influencing factors. Then, this paper proposed a novel forecasting model named e‐VMD‐PCA‐RELM. The algorithm is based on the traditional RELM (robust extreme learning machine) algorithm, which effectively processes the noise information through the PCA (principal component analysis) algorithm, and extracted the feature elements of the RELM hidden layer to reduce the information redundancy. At the same time, the e‐VMD (variational mode decomposition optimized by entropy) algorithm is used to decompose the original time series to obtain multiple components. By comparing with the other forecasting algorithms, it is proved that the proposed forecasting model has strong generalization ability and has achieved good prediction result. Finally, the e‐VMD‐PCA‐RELM model is used to predict the scale of offshore wind farms in China from 2019 to 2035. We find that the cumulative installed capacity of China's offshore wind power will exceed 60 GW in 2035, and the installed capacity will increase year by year. In 2030, there will be a large increase, with a relative growth rate of 20%." @default.
• W3039339675 created "2020-07-10" @default.
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• W3039339675 date "2020-07-06" @default.
• W3039339675 modified "2023-09-26" @default.
• W3039339675 title "Future prospects research on offshore wind power scale in China based on signal decomposition and extreme learning machine optimized by principal component analysis" @default.
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• W3039339675 doi "https://doi.org/10.1002/ese3.761" @default.
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