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Mid-term interval load forecasting using multi-output support vector regression with a memetic algorithm for feature selection

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  • Hu, Zhongyi
  • Bao, Yukun
  • Chiong, Raymond
  • Xiong, Tao

Abstract

Accurate forecasting of mid-term electricity load is an important issue for power system planning and operation. Instead of point load forecasting, this study aims to model and forecast mid-term interval loads up to one month in the form of interval-valued series consisting of both peak and valley points by using MSVR (Multi-output Support Vector Regression). In addition, an MA (Memetic Algorithm) based on the firefly algorithm is used to select proper input features among the feature candidates, which include time lagged loads as well as temperatures. The capability of this proposed interval load modeling and forecasting framework to predict daily interval electricity demands is tested through simulation experiments using real-world data from North America and Australia. Quantitative and comprehensive assessments are performed and the experimental results show that the proposed MSVR-MA forecasting framework may be a promising alternative for interval load forecasting.

Suggested Citation

  • Hu, Zhongyi & Bao, Yukun & Chiong, Raymond & Xiong, Tao, 2015. "Mid-term interval load forecasting using multi-output support vector regression with a memetic algorithm for feature selection," Energy, Elsevier, vol. 84(C), pages 419-431.
    • Handle: RePEc:eee:energy:v:84:y:2015:i:c:p:419-431
    DOI: 10.1016/j.energy.2015.03.054
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    1. Amarawickrama, Himanshu A. & Hunt, Lester C., 2008. "Electricity demand for Sri Lanka: A time series analysis," Energy, Elsevier, vol. 33(5), pages 724-739.
    2. Javier Arroyo & Rosa Espínola & Carlos Maté, 2011. "Different Approaches to Forecast Interval Time Series: A Comparison in Finance," Computational Economics, Springer;Society for Computational Economics, vol. 37(2), pages 169-191, February.
    3. Wang, Chi-hsiang & Grozev, George & Seo, Seongwon, 2012. "Decomposition and statistical analysis for regional electricity demand forecasting," Energy, Elsevier, vol. 41(1), pages 313-325.
    4. Xiong, Tao & Bao, Yukun & Hu, Zhongyi, 2013. "Beyond one-step-ahead forecasting: Evaluation of alternative multi-step-ahead forecasting models for crude oil prices," Energy Economics, Elsevier, vol. 40(C), pages 405-415.
    5. Yan-Leung Cheung & Yin-Wong Cheung & Alan T. K. Wan, 2009. "A high-low model of daily stock price ranges," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(2), pages 103-119.
    6. Ben Taieb, Souhaib & Hyndman, Rob J., 2014. "A gradient boosting approach to the Kaggle load forecasting competition," International Journal of Forecasting, Elsevier, vol. 30(2), pages 382-394.
    7. Goia, Aldo & May, Caterina & Fusai, Gianluca, 2010. "Functional clustering and linear regression for peak load forecasting," International Journal of Forecasting, Elsevier, vol. 26(4), pages 700-711, October.
    8. Yin-Wong Cheung, 2007. "An empirical model of daily highs and lows," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 12(1), pages 1-20.
    9. Hahn, Heiko & Meyer-Nieberg, Silja & Pickl, Stefan, 2009. "Electric load forecasting methods: Tools for decision making," European Journal of Operational Research, Elsevier, vol. 199(3), pages 902-907, December.
    10. Maia, André Luis Santiago & de Carvalho, Francisco de A.T., 2011. "Holt’s exponential smoothing and neural network models for forecasting interval-valued time series," International Journal of Forecasting, Elsevier, vol. 27(3), pages 740-759.
    11. Abu-Shikhah, Nazih & Elkarmi, Fawwaz, 2011. "Medium-term electric load forecasting using singular value decomposition," Energy, Elsevier, vol. 36(7), pages 4259-4271.
    12. García-Ascanio, Carolina & Maté, Carlos, 2010. "Electric power demand forecasting using interval time series: A comparison between VAR and iMLP," Energy Policy, Elsevier, vol. 38(2), pages 715-725, February.
    13. Tao Xiong & Yukun Bao & Zhongyi Hu, 2014. "Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting," Papers 1401.1916, arXiv.org.
    14. Maia, André Luis Santiago & de Carvalho, Francisco de A.T., 2011. "Holt's exponential smoothing and neural network models for forecasting interval-valued time series," International Journal of Forecasting, Elsevier, vol. 27(3), pages 740-759, July.
    15. Saab, Samer & Badr, Elie & Nasr, George, 2001. "Univariate modeling and forecasting of energy consumption: the case of electricity in Lebanon," Energy, Elsevier, vol. 26(1), pages 1-14.
    Full references (including those not matched with items on IDEAS)

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    21. Bashiri Behmiri, Niaz & Fezzi, Carlo & Ravazzolo, Francesco, 2023. "Incorporating air temperature into mid-term electricity load forecasting models using time-series regressions and neural networks," Energy, Elsevier, vol. 278(C).

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