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Study on Selecting the Optimal Algorithm and the Effective Methodology to ANN-Based Short-Term Load Forecasting Model for the Southern Power Company in Vietnam

Author

Listed:
  • Manh-Hai Pham

    (Faculty of Electrical Engineering, Electrical Power University, Hanoi 11355, Vietnam)

  • T-A-Tho Vu

    (Faculty of Electrical Engineering, Electrical Power University, Hanoi 11355, Vietnam)

  • Duc-Quang Nguyen

    (Faculty of Electrical Engineering, Electrical Power University, Hanoi 11355, Vietnam)

  • Viet-Hung Dang

    (Faculty of Electrical Engineering, Electrical Power University, Hanoi 11355, Vietnam)

  • Ngoc-Trung Nguyen

    (Faculty of Electrical Engineering, Electrical Power University, Hanoi 11355, Vietnam)

  • Thu-Huyen Dang

    (Faculty of Electrical Engineering, Electrical Power University, Hanoi 11355, Vietnam)

  • The Vinh Nguyen

    (Quang Ninh University of Industry, Quang Ninh 02451 Vietnam)

Abstract

Recently, power companies apply optimal algorithms for short-term load forecasting, especially the daily load. However, in Vietnam, the load forecasting of the power system has not focused on this solution. Optimal algorithms and can help experts improve forecasting results including accuracy and the time required for forecasting. To achieve both goals, the combinations of different algorithms are still being studied. This article describes research using a new combination of two optimal algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). This combination limits the weakness of the convergence speed of GA as well as the weakness of PSO that it easily falls into local optima (thereby reducing accuracy). This new hybrid algorithm was applied to the Southern Power Corporation’s (SPC—a large Power company in Vietnam) daily load forecasting. The results show the algorithm’s potential to provide a solution. The most accurate result was for the forecasting of a normal working day with an average error of 1.15% while the largest error was 3.74% and the smallest was 0.02%. For holidays and weekends, the average error always approximated the allowable limit of 3%. On the other hand, some poor results also provide an opportunity to re-check the real data provided by SPC.

Suggested Citation

  • Manh-Hai Pham & T-A-Tho Vu & Duc-Quang Nguyen & Viet-Hung Dang & Ngoc-Trung Nguyen & Thu-Huyen Dang & The Vinh Nguyen, 2019. "Study on Selecting the Optimal Algorithm and the Effective Methodology to ANN-Based Short-Term Load Forecasting Model for the Southern Power Company in Vietnam," Energies, MDPI, vol. 12(12), pages 1-19, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:12:p:2283-:d:239933
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    References listed on IDEAS

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    3. Zhang, Qiang & Ogren, Ryan M. & Kong, Song-Charng, 2016. "A comparative study of biodiesel engine performance optimization using enhanced hybrid PSO–GA and basic GA," Applied Energy, Elsevier, vol. 165(C), pages 676-684.
    4. Garg, Harish, 2016. "A hybrid PSO-GA algorithm for constrained optimization problems," Applied Mathematics and Computation, Elsevier, vol. 274(C), pages 292-305.
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    Cited by:

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