IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i12p2283-d239933.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/12/2283/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/12/2283/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. AlRashidi, M.R. & EL-Naggar, K.M., 2010. "Long term electric load forecasting based on particle swarm optimization," Applied Energy, Elsevier, vol. 87(1), pages 320-326, January.
    2. Alobaidi, Mohammad H. & Chebana, Fateh & Meguid, Mohamed A., 2018. "Robust ensemble learning framework for day-ahead forecasting of household based energy consumption," Applied Energy, Elsevier, vol. 212(C), pages 997-1012.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Eduardo Caro & Jesús Juan, 2020. "Short-Term Load Forecasting for Spanish Insular Electric Systems," Energies, MDPI, vol. 13(14), pages 1-26, July.
    2. Anderson Passos de Aragão & Patrícia Teixeira Leite Asano & Ricardo de Andrade Lira Rabêlo, 2020. "A Reservoir Operation Policy Using Inter-Basin Water Transfer for Maximizing Hydroelectric Benefits in Brazil," Energies, MDPI, vol. 13(10), pages 1-26, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fanidhar Dewangan & Almoataz Y. Abdelaziz & Monalisa Biswal, 2023. "Load Forecasting Models in Smart Grid Using Smart Meter Information: A Review," Energies, MDPI, vol. 16(3), pages 1-55, January.
    2. Byungsung Lee & Haesung Lee & Hyun Ahn, 2020. "Improving Load Forecasting of Electric Vehicle Charging Stations Through Missing Data Imputation," Energies, MDPI, vol. 13(18), pages 1-15, September.
    3. Langevin, J. & Reyna, J.L. & Ebrahimigharehbaghi, S. & Sandberg, N. & Fennell, P. & Nägeli, C. & Laverge, J. & Delghust, M. & Mata, É. & Van Hove, M. & Webster, J. & Federico, F. & Jakob, M. & Camaras, 2020. "Developing a common approach for classifying building stock energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    4. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    5. Fang, Lei & He, Bin, 2023. "A deep learning framework using multi-feature fusion recurrent neural networks for energy consumption forecasting," Applied Energy, Elsevier, vol. 348(C).
    6. Afzal, Asif & Buradi, Abdulrajak & Jilte, Ravindra & Shaik, Saboor & Kaladgi, Abdul Razak & Arıcı, Muslum & Lee, Chew Tin & Nižetić, Sandro, 2023. "Optimizing the thermal performance of solar energy devices using meta-heuristic algorithms: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    7. Al-Sulttani, Ali O. & Ahsan, Amimul & Hanoon, Ammar N. & Rahman, A. & Daud, N.N.N. & Idrus, S., 2017. "Hourly yield prediction of a double-slope solar still hybrid with rubber scrapers in low-latitude areas based on the particle swarm optimization technique," Applied Energy, Elsevier, vol. 203(C), pages 280-303.
    8. Sultana, U. & Khairuddin, Azhar B. & Sultana, Beenish & Rasheed, Nadia & Qazi, Sajid Hussain & Malik, Nimra Riaz, 2018. "Placement and sizing of multiple distributed generation and battery swapping stations using grasshopper optimizer algorithm," Energy, Elsevier, vol. 165(PA), pages 408-421.
    9. Chen, Guangwu & Zhu, Yuhan & Wiedmann, Thomas & Yao, Lina & Xu, Lixiao & Wang, Yafei, 2019. "Urban-rural disparities of household energy requirements and influence factors in China: Classification tree models," Applied Energy, Elsevier, vol. 250(C), pages 1321-1335.
    10. Jiyang Wang & Yuyang Gao & Xuejun Chen, 2018. "A Novel Hybrid Interval Prediction Approach Based on Modified Lower Upper Bound Estimation in Combination with Multi-Objective Salp Swarm Algorithm for Short-Term Load Forecasting," Energies, MDPI, vol. 11(6), pages 1-30, June.
    11. Ke, Ming-Tsun & Yeh, Chia-Hung & Su, Cheng-Jie, 2017. "Cloud computing platform for real-time measurement and verification of energy performance," Applied Energy, Elsevier, vol. 188(C), pages 497-507.
    12. Younghoon Lee, 2022. "Identifying Competitive Attributes Based on an Ensemble of Explainable Artificial Intelligence," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 64(4), pages 407-419, August.
    13. Asif Afzal & Saad Alshahrani & Abdulrahman Alrobaian & Abdulrajak Buradi & Sher Afghan Khan, 2021. "Power Plant Energy Predictions Based on Thermal Factors Using Ridge and Support Vector Regressor Algorithms," Energies, MDPI, vol. 14(21), pages 1-22, November.
    14. Lin, Whei-Min & Gow, Hong-Jey & Tsai, Ming-Tang, 2010. "An enhanced radial basis function network for short-term electricity price forecasting," Applied Energy, Elsevier, vol. 87(10), pages 3226-3234, October.
    15. Adane Abebaw Gessesse & Rajashree Mishra & Mitali Madhumita Acharya & Kedar Nath Das, 2020. "Genetic algorithm based fuzzy programming approach for multi-objective linear fractional stochastic transportation problem involving four-parameter Burr distribution," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(1), pages 93-109, February.
    16. Taslimi-Renani, Ehsan & Modiri-Delshad, Mostafa & Elias, Mohamad Fathi Mohamad & Rahim, Nasrudin Abd., 2016. "Development of an enhanced parametric model for wind turbine power curve," Applied Energy, Elsevier, vol. 177(C), pages 544-552.
    17. Yassin Belkourchia & Mohamed Zeriab Es-Sadek & Lahcen Azrar, 2023. "New Hybrid Perturbed Projected Gradient and Simulated Annealing Algorithms for Global Optimization," Journal of Optimization Theory and Applications, Springer, vol. 197(2), pages 438-475, May.
    18. 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.
    19. Touqeer Ahmed Jumani & Mohd Wazir Mustafa & Nawaf N. Hamadneh & Samer H. Atawneh & Madihah Md. Rasid & Nayyar Hussain Mirjat & Muhammad Akram Bhayo & Ilyas Khan, 2020. "Computational Intelligence-Based Optimization Methods for Power Quality and Dynamic Response Enhancement of ac Microgrids," Energies, MDPI, vol. 13(16), pages 1-22, August.
    20. Chakraborty, Debaditya & Alam, Arafat & Chaudhuri, Saptarshi & Başağaoğlu, Hakan & Sulbaran, Tulio & Langar, Sandeep, 2021. "Scenario-based prediction of climate change impacts on building cooling energy consumption with explainable artificial intelligence," Applied Energy, Elsevier, vol. 291(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:12:p:2283-:d:239933. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.