首页|Reports on Machine Learning from Zhejiang University Provide New Insights (Machine Learning-based Probabilistic Forecasting of Wind Power Generation: a Combined Bootstrap and Cumulant Method)
Reports on Machine Learning from Zhejiang University Provide New Insights (Machine Learning-based Probabilistic Forecasting of Wind Power Generation: a Combined Bootstrap and Cumulant Method)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
IEEE
Researchers detail new data in Machine Learning. According to news originating from Hangzhou, People's Republic of China, by NewsRx correspondents, research stated, "Probabilistic forecasting provides complete probability information of renewable generation and load, which assists the diverse decision-making tasks in power systems under uncertainties. Conventional machine learning-based probabilistic forecasting methods usually consider the predictive uncertainty following prior distributional assumptions." Our news journalists obtained a quote from the research from Zhejiang University, "This article develops a novel combined bootstrap and cumulant (CBC) method to generate nonparametric predictive distribution using higher order statistics for probabilistic forecasting. The CBC method successfully integrates machine learning with conditional moments and cumulants to describe the overall predictive uncertainty. A bootstrap-based conditional moment estimation method is proposed to quantify both the epistemic and aleatory uncertainties involved in machine learning. Higher order cumulants are utilized for overall uncertainty quantification based on the estimated conditional moments with its unique additivity. Three types of series expansions including Gram-Charlier, Edgeworth, and Cornish-Fisher expansions are adopted to improve the overall performance and the generalization ability."
HangzhouPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningZhejiang University
NETL
NSTL
IEEE
