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.
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Acknowledgments
This work was supported by the “Application platform and Industrialization for efficient cloud computing for Big data” of the Science and Technology Supported Program of Jiangsu Province(BA2015052).
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Zheng, H., Yuan, J., Zhao, C. (2017). Multi-output LSSVM-Based Forecasting Model for Mid-Term Interval Load Optimized by SOA and Fresh Degree Function. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_8
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DOI: https://doi.org/10.1007/978-3-319-68935-7_8
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