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Accurate Forecast Improvement Approach for Short Term Load Forecasting Using Hybrid Filter-Wrap Feature Selection

Author

Listed:
  • Samuel Atuahene

    (Center for Modern Information Management, School of Management, Huazhong University of Science and Technology, Wuhan-China)

  • Yukun Bao

    (Center for Modern Information Management, School of Management, Huazhong University of Science and Technology, Wuhan-China)

  • Patricia Semwaah Gyan

    (Faculty of Earth Resources, China University of Geosciences, Wuhan-China)

  • Yao Yevenyo Ziggah

    (Department of Geomatic Engineering, University of Mines and Technology, Tarkwa, Ghana)

Abstract

Accurate hybrid ï¬ lter– wrap approach is quite important for short term load forecasting as it not only improve forecasting accuracy performance, but also could effectively avoid converging prematurely. The importance of input selection-features is an essential part to develop models. Currently and dynamic surroundings, energy demand, quantity and values are becoming unpredictable and progressively volatile. Increasing amount of decision-making procedures in the industries in terms of energy require a wide-ranging outlook of the uncertain forthcoming. This paper explains the selection method for the proposed hybrid filter-wrapper whose primary composition includes Personal Modular Impactor (PMI) based filter technique and the Firefly Algorithm (FA) based filter wrapper. The filter wrapper planning technique involves the selection of the best corresponding inputs by a predefined model-free technique that measures the specific relationship between the output selection and the input variable. FA wrapper based technique is more useful compared to the filter procedure. Modular Impactor (MI) is a technique mostly preferred by individuals to measure the dependency of variables and commonly used to select input features and in other key fields.

Suggested Citation

  • Samuel Atuahene & Yukun Bao & Patricia Semwaah Gyan & Yao Yevenyo Ziggah, 2019. "Accurate Forecast Improvement Approach for Short Term Load Forecasting Using Hybrid Filter-Wrap Feature Selection," International Journal of Management Science and Business Administration, Inovatus Services Ltd., vol. 5(2), pages 37-49, January.
    • Handle: RePEc:mgs:ijmsba:v:5:y:2019:i:2:p:37-49
    DOI: 10.18775/ijmsba.1849-5664-5419.2014.52.1004
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    References listed on IDEAS

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    Cited by:

    1. Shi, Jiaqi & Li, Chenxi & Yan, Xiaohe, 2023. "Artificial intelligence for load forecasting: A stacking learning approach based on ensemble diversity regularization," Energy, Elsevier, vol. 262(PB).

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    More about this item

    Keywords

    Load Forecasting; Energy Forecast; Personal Modular Impactor; Firefly Algorithm;
    All these keywords.

    JEL classification:

    • M00 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - General - - - General

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