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Equipping Seasonal Exponential Smoothing Models with Particle Swarm Optimization Algorithm for Electricity Consumption Forecasting

Author

Listed:
  • Changrui Deng

    (Center of Big Data Analytics, Jiangxi University of Engineering, Xinyu 338029, China)

  • Xiaoyuan Zhang

    (Center of Big Data Analytics, Jiangxi University of Engineering, Xinyu 338029, China)

  • Yanmei Huang

    (Center of Big Data Analytics, Jiangxi University of Engineering, Xinyu 338029, China)

  • Yukun Bao

    (Center for Modern Information Management, School of Management, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

Electricity consumption forecasting plays an important role in investment planning of electricity infrastructure, and in electricity production/generation and distribution. Accurate electricity consumption prediction over the mid/long term is of great interest to both practitioners and academics. Considering that monthly electricity consumption series usually show an obvious seasonal variation due to their inherent nature subject to temperature during the year, in this paper, seasonal exponential smoothing (SES) models were employed as the modeling technique, and the particle swarm optimization (PSO) algorithm was applied to find a set of near-optimal smoothing parameters. Quantitative and comprehensive assessments were performed with two real-world electricity consumption datasets on the basis of prediction accuracy and computational cost. The experimental results indicated that (1) whether the accuracy measure or the elapsed time was considered, the PSO performed better than grid search (GS) or genetic algorithm (GA); (2) the proposed PSO-based SES model with a non-trend component and additive seasonality term significantly outperformed other competitors for the majority of prediction horizons, which indicates that the model could be a promising alternative for electricity consumption forecasting.

Suggested Citation

  • Changrui Deng & Xiaoyuan Zhang & Yanmei Huang & Yukun Bao, 2021. "Equipping Seasonal Exponential Smoothing Models with Particle Swarm Optimization Algorithm for Electricity Consumption Forecasting," Energies, MDPI, vol. 14(13), pages 1-14, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:13:p:4036-:d:588369
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    References listed on IDEAS

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    3. Zhou, Wenhao & Li, Hailin & Zhang, Zhiwei, 2022. "A novel seasonal fractional grey model for predicting electricity demand: A case study of Zhejiang in China," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 200(C), pages 128-147.
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