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Forecasting Fossil Fuel Energy Consumption for Power Generation Using QHSA-Based LSSVM Model

Author

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  • Wei Sun

    (School of Economics and Management, North China Electric Power University, Baoding 071003, Hebei, China)

  • Yujun He

    (Department of Electronic & Communication Engineering, North China Electric Power University, Baoding 071003, Hebei, China)

  • Hong Chang

    (Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Shanghai 200240, China)

Abstract

Accurate forecasting of fossil fuel energy consumption for power generation is important and fundamental for rational power energy planning in the electricity industry. The least squares support vector machine (LSSVM) is a powerful methodology for solving nonlinear forecasting issues with small samples. The key point is how to determine the appropriate parameters which have great effect on the performance of LSSVM model. In this paper, a novel hybrid quantum harmony search algorithm-based LSSVM (QHSA-LSSVM) energy forecasting model is proposed. The QHSA which combines the quantum computation theory and harmony search algorithm is applied to searching the optimal values of and C in LSSVM model to enhance the learning and generalization ability. The case study on annual fossil fuel energy consumption for power generation in China shows that the proposed model outperforms other four comparative models, namely regression, grey model (1, 1) (GM (1, 1)), back propagation (BP) and LSSVM, in terms of prediction accuracy and forecasting risk.

Suggested Citation

  • Wei Sun & Yujun He & Hong Chang, 2015. "Forecasting Fossil Fuel Energy Consumption for Power Generation Using QHSA-Based LSSVM Model," Energies, MDPI, vol. 8(2), pages 1-21, January.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:2:p:939-959:d:45215
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    References listed on IDEAS

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    Cited by:

    1. Vincenzo Dovì & Antonella Battaglini, 2015. "Energy Policy and Climate Change: A Multidisciplinary Approach to a Global Problem," Energies, MDPI, vol. 8(12), pages 1-8, November.
    2. Xiwen Cui & Shaojun E & Dongxiao Niu & Dongyu Wang & Mingyu Li, 2021. "An Improved Forecasting Method and Application of China’s Energy Consumption under the Carbon Peak Target," Sustainability, MDPI, vol. 13(15), pages 1-21, August.

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