文章基本信息

标题：Employing Best Input SVR Robust Lost Function with Nature-Inspired Metaheuristics in Wind Speed Energy Forecasting
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作者：Rezzy Eko Caraka ; Rung Ching Chen ; Sakhinah Abu Bakar 等
期刊名称：IAENG International Journal of Computer Science
印刷版ISSN：1819-656X
电子版ISSN：1819-9224
出版年度：2020
卷号：47
期号：3
语种：English
出版社：IAENG - International Association of Engineers
摘要：Wind power has been experiencing a quick improvement. Without a doubt, wind is a variable asset that is hard to forecast. For instance, traditionally time series, extra holds are distributed to deal with this uncertainty. This paper presents a comparison of the performance of various Support Vector Regression (SVR) applied to short-term wind power forecasting. The analogy with BORUTA and multivariate adaptive regression splines (MARS) as judge best input and employ genetic algorithm and particle swarm optimization to find best parameter in Support Vector Regression with robust lost function. We measure the accuracy of this models by Symmetric means absolute percentage error (sMAPE) and we get the best model BORUTA-SVR-PSO with sMAPE 2.07155%. Moreover, we measure the energy conversion using Feedback Linearization Control (FLC).
关键词：Wind Speed;Feature Importance;Boruta;SVR;Genetic Algorithm;Particle Swarm Optimization