Future prospects research on offshore wind power scale in China based on signal decomposition and extreme learning machine optimized by principal component analysis

Abstract

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

A new triboelectric nanogenerator based on a multi-material stacking structure achieves efficient power conversion from discrete mechanical movement

“…Generally, structural changes are aimed at increasing the charge density on the surface or reaching a charge-excited operating state to achieve a significant improvement in performance. 20–24 In this work, a novel design based on a three-layer stacked structure working in the vertical contact separation mode was developed to obtain optimal results. The working principle of PFP-TENG is illustrated in Fig.…”

Offshore wind speed short-term forecasting based on a hybrid method: Swarm decomposition and meta-extreme learning machine

“…Thanks to fast response, Extreme Learning Machine (ELM)-based models have become increasingly popular in wind energy forecasting in recent years [26], [27], [28], [29]. Liu et al [30] proposed a hybrid forecasting model based on Robust ELM (RELM) to predict the cumulative capacity of offshore wind power installed in China in the future. The stand-alone RELM algorithm was not as good as that of the Least-Squares Support Vector Machine (LSSVM), but it can be greatly improved with hybrid algorithms such as decomposition techniques.…”

Forecast Research on Multidimensional Influencing Factors of Global Offshore Wind Power Investment Based on Random Forest and Elastic Net

“…At present, the published literature on global offshore wind power projects mainly focuses on project cost and management strategy [7][8][9] and there is little research that pays attention to the overall project investment forecast. Chinmoy et al (2019) took the comprehensive cost into consideration and built a clean energy collaborative optimization model from an overall perspective [10].…”