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Hybridizing QCM with Dragonfly Algorithm to Enrich the Solution Searching Behaviors

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Hybrid Intelligent Technologies in Energy Demand Forecasting

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Abstract

As indicated in Chap. 4 that hybridizing different meta-heuristic algorithms [including gravitational search algorithm (GSA), cuckoo search algorithm (CSA), bat algorithm (BA), and fruit fly optimization algorithm (FOA)] with an SVR-based electric load forecasting model can receive superior forecasting performance than other competitive forecasting models (including ARIMA, HW, GRNN, and BPNN models).

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Authors and Affiliations

  1. Department of Information Management, Oriental Institute of Technology, New Taipei, Taiwan

    Wei-Chiang Hong

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Hong, WC. (2020). Hybridizing QCM with Dragonfly Algorithm to Enrich the Solution Searching Behaviors. In: Hybrid Intelligent Technologies in Energy Demand Forecasting. Springer, Cham. https://doi.org/10.1007/978-3-030-36529-5_5

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  • DOI: https://doi.org/10.1007/978-3-030-36529-5_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36528-8

  • Online ISBN: 978-3-030-36529-5

  • eBook Packages: EnergyEnergy (R0)

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