Application of seasonal SVR with chaotic gravitational search algorithm in electricity forecasting

DOI10.1016/j.apm.2013.05.016zbMath1428.62500OpenAlexW2070101089MaRDI QIDQ1792298

Fuh-Yuh Ju, Wei-Chiang Hong

Publication date: 11 October 2018

Published in: Applied Mathematical Modelling (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.apm.2013.05.016

zbMATH Keywords

support vector regression (SVR)chaotic gravitational search algorithm (CGSA)electricity forecastingseasonal mechanism

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Approximation methods and heuristics in mathematical programming (90C59)

Related Items (2)

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Uses Software

• GSA

Cites Work

• Forecasting realized volatility using a long-memory stochastic volatility model: estimation, prediction and seasonal adjustment
• Rainfall forecasting by technological machine learning models
• Electric load forecasting by support vector model
• Practical selection of SVM parameters and noise estimation for SVM regression
• GSA: A gravitational search algorithm
• Improved particle swarm optimization combined with chaos
• Simple mathematical models with very complicated dynamics
• Electric Load Forecasting

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