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A novel hybrid model based on neural network and multi-objective optimization for effective load forecast

Author

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  • Singh, Priyanka
  • Dwivedi, Pragya

Abstract

In recent years, increased attention has been paid by the researchers to predict accurate and stable load due to its effect on the economy and need for proper management of power systems. However, most of the previous research focused only on either reducing load forecast error or enhancing the stability, very few studies focused on these two issues simultaneously. Introducing a forecasting model to solve both independent objectives at the same time is a challenging task due to the complex behavior of the load pattern.

Suggested Citation

  • Singh, Priyanka & Dwivedi, Pragya, 2019. "A novel hybrid model based on neural network and multi-objective optimization for effective load forecast," Energy, Elsevier, vol. 182(C), pages 606-622.
    • Handle: RePEc:eee:energy:v:182:y:2019:i:c:p:606-622
    DOI: 10.1016/j.energy.2019.06.075
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    Citations

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    Cited by:

    1. Park, Musik & Wang, Zhiyuan & Li, Lanyu & Wang, Xiaonan, 2023. "Multi-objective building energy system optimization considering EV infrastructure," Applied Energy, Elsevier, vol. 332(C).
    2. Wenhao Chen & Guangjie Han & Hongbo Zhu & Lyuchao Liao & Wenqing Zhao, 2022. "Deep ResNet-Based Ensemble Model for Short-Term Load Forecasting in Protection System of Smart Grid," Sustainability, MDPI, vol. 14(24), pages 1-17, December.
    3. Jiang, Weiheng & Wu, Xiaogang & Gong, Yi & Yu, Wanxin & Zhong, Xinhui, 2020. "Holt–Winters smoothing enhanced by fruit fly optimization algorithm to forecast monthly electricity consumption," Energy, Elsevier, vol. 193(C).
    4. Wang, Pengfei & Zhu, Ze & Liang, Wenlong & Liao, Longtao & Wan, Jiashuang, 2023. "Hybrid mechanistic and neural network modeling of nuclear reactors," Energy, Elsevier, vol. 282(C).
    5. Tayab, Usman Bashir & Zia, Ali & Yang, Fuwen & Lu, Junwei & Kashif, Muhammad, 2020. "Short-term load forecasting for microgrid energy management system using hybrid HHO-FNN model with best-basis stationary wavelet packet transform," Energy, Elsevier, vol. 203(C).
    6. Jiang, Hongyan & Cheng, Feng & Wu, Cong & Fang, Dianjun & Zeng, Yuhai, 2024. "A multi-period-sequential-index combination method for short-term prediction of small sample data," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    7. Navarkar, Abhishek & Hasti, Veeraraghava Raju & Deneke, Elihu & Gore, Jay P., 2020. "A data-driven model for thermodynamic properties of a steam generator under cycling operation," Energy, Elsevier, vol. 211(C).
    8. Lalitpat Aswanuwath & Warut Pannakkong & Jirachai Buddhakulsomsiri & Jessada Karnjana & Van-Nam Huynh, 2023. "A Hybrid Model of VMD-EMD-FFT, Similar Days Selection Method, Stepwise Regression, and Artificial Neural Network for Daily Electricity Peak Load Forecasting," Energies, MDPI, vol. 16(4), pages 1-24, February.
    9. Loau Al-Bahrani & Mehdi Seyedmahmoudian & Ben Horan & Alex Stojcevski, 2021. "Solving the Real Power Limitations in the Dynamic Economic Dispatch of Large-Scale Thermal Power Units under the Effects of Valve-Point Loading and Ramp-Rate Limitations," Sustainability, MDPI, vol. 13(3), pages 1-26, January.
    10. Gabriel Trierweiler Ribeiro & João Guilherme Sauer & Naylene Fraccanabbia & Viviana Cocco Mariani & Leandro dos Santos Coelho, 2020. "Bayesian Optimized Echo State Network Applied to Short-Term Load Forecasting," Energies, MDPI, vol. 13(9), pages 1-19, May.
    11. Kottath, Rahul & Singh, Priyanka, 2023. "Influencer buddy optimization: Algorithm and its application to electricity load and price forecasting problem," Energy, Elsevier, vol. 263(PC).

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