Fuzzy Load Forecast with Optimized Parametric Adjustment Using Jaya Optimization Algorithm

This paper proposes an advanced fuzzy load forecast method optimized by modified Jaya optimization (MJO) algorithm. MATLAB® platform is used to implement the proposed hybrid Fuzzy-MJO load forecasting algorithm and to verify the outperforming features of a Jaya technique over a fuzzy load forecast m...

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Veröffentlicht in:International journal of computational intelligence systems Jg. 13; H. 1; S. 875 - 892
1. Verfasser: Anh, Ho Pham Huy
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Dordrecht Springer Netherlands 01.01.2020
Springer Nature B.V
Springer
Schlagworte:
Advanced fuzzy load forecasting model
Algorithms
Electricity consumption
Forecasting
Hybrid fuzzy-MJO load forecasting algorithm
Mathematical models
Minimal peak energy error
Minimal total energy error
Modified Jaya optimization (MJO) algorithm
Optimization
Power consumption
Research Article
Minimal peak energy error
Minimal total energy error
Hybrid fuzzy-MJO load forecasting algorithm
Advanced fuzzy load forecasting model
Modified Jaya optimization (MJO) algorithm
ISSN:1875-6891, 1875-6883
Online-Zugang:Volltext
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Zusammenfassung:This paper proposes an advanced fuzzy load forecast method optimized by modified Jaya optimization (MJO) algorithm. MATLAB® platform is used to implement the proposed hybrid Fuzzy-MJO load forecasting algorithm and to verify the outperforming features of a Jaya technique over a fuzzy load forecast model. The novel Fuzzy-MJO load forecasting systems uses the day-time and the daily power consumption to efficiently predict the forecast power consumption. The comparative load forecasting results between proposed Fuzzy-MJO with the latest other algorithms are adequately presented. The full week forecast results using proposed hybrid Fuzzy-MJO load forecasting algorithm demonstrates an outperforming superiority, through the various tested cases, regarding to the total and the peak power error in comparison with the fuzzy-based load forecast model.
Bibliographie:ObjectType-Article-1
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ISSN:1875-6891
1875-6883
DOI:10.2991/ijcis.d.200617.002