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Erschienen in:
2017 | OriginalPaper | Buchkapitel
Multi-output LSSVM-Based Forecasting Model for Mid-Term Interval Load Optimized by SOA and Fresh Degree Function
verfasst von : Huiting Zheng, Jiabin Yuan, Chang Zhao
Erschienen in: Intelligent Data Engineering and Automated Learning – IDEAL 2017
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Abstract
Accurate forecasting of mid-term electricity load is an important issue for risk management when making power system planning and operational decisions. In this study we have proposed an interval-valued load forecasting model called SOA-FD-MLSSVM. The proposed model consists of three components, the Human Body Amenity(HBA) indicator is introduced as the input of meteorological factors, Fresh Degree(FD) function is brought into the forecast method based on setting different weight on the historical days and Least Squares Support Vector Machine based on Multi-Output model, called MLSSVM, to make simultaneous interval-valued forecasts. Moreover, the MLSSVM parameters are optimized by a novel seeker optimization algorithm(SOA). Simulations carried out on the electricity markets data from Jiangsu province. Analytical results show that the novel optimized prediction model is superior to others listed algorithms in predicting interval-valued loads with lower \({U^I}\), \(AR{V^I}\) and MAPE.