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- Title
Employing Best Input SVR Robust Lost Function with Nature-Inspired Metaheuristics in Wind Speed Energy Forecasting.
- Authors
Rezzy Eko Caraka; Rung Ching Chen; Bakar, Sakhinah Abu; Tahmid, Muhammad; Toharudin, Toni; Pardamean, Bens; Su-Wen Huang
- Abstract
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).
- Subjects
WIND speed; WIND power; PARTICLE swarm optimization; ENERGY consumption; WIND forecasting; GENETIC algorithms; METAHEURISTIC algorithms
- Publication
IAENG International Journal of Computer Science, 2020, Vol 47, Issue 3, p83
- ISSN
1819-656X
- Publication type
Article