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Short-term load forecasting for microgrid energy management system using hybrid HHO-FNN model with best-basis stationary wavelet packet transform

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  • Tayab, Usman Bashir
  • Zia, Ali
  • Yang, Fuwen
  • Lu, Junwei
  • Kashif, Muhammad

Abstract

Accurate prediction of load has become one of the most crucial issue in the energy management system of the microgrid. Therefore, a precise load forecasting tool is necessary for efficient power management in the microgrid, which can lead to economic benefits for consumers and power industries. This paper proposes a hybrid approach for short-term forecasting of load demand in a typical microgrid, which is a combination of the best-basis stationary wavelet packet transform and Harris hawks optimization-based feed-forward neural network. The Harris hawks optimization is applied to the feed-forward neural network as an alternative training algorithm for optimizing the weight and basis of neurons. The proposed model is applied to predict load demand in the Queensland electric market and is compared with existing competitive models. Numerical results are obtained using MATLAB. These results demonstrate that the proposed approach reduces the average mean absolute percentage error by 33.30%, 49.54% and 60.76% as compared to the particle swarm optimization (PSO) based artificial neural network, PSO based least-square-support vector machine and back-propagation based neural network, respectively.

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  • 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).
    • Handle: RePEc:eee:energy:v:203:y:2020:i:c:s0360544220309646
    DOI: 10.1016/j.energy.2020.117857
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    15. Karodine Chreng & Han Soo Lee & Soklin Tuy, 2022. "A Hybrid Model for Electricity Demand Forecast Using Improved Ensemble Empirical Mode Decomposition and Recurrent Neural Networks with ERA5 Climate Variables," Energies, MDPI, vol. 15(19), pages 1-26, October.
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    17. Yang, Wendong & Sun, Shaolong & Hao, Yan & Wang, Shouyang, 2022. "A novel machine learning-based electricity price forecasting model based on optimal model selection strategy," Energy, Elsevier, vol. 238(PC).
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    19. Umar Javed & Khalid Ijaz & Muhammad Jawad & Ejaz A. Ansari & Noman Shabbir & Lauri Kütt & Oleksandr Husev, 2021. "Exploratory Data Analysis Based Short-Term Electrical Load Forecasting: A Comprehensive Analysis," Energies, MDPI, vol. 14(17), pages 1-22, September.
    20. Ziad M. Ali & Martin Calasan & Shady H. E. Abdel Aleem & Francisco Jurado & Foad H. Gandoman, 2023. "Applications of Energy Storage Systems in Enhancing Energy Management and Access in Microgrids: A Review," Energies, MDPI, vol. 16(16), pages 1-41, August.
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    22. Akash Kumar & Bing Yan & Ace Bilton, 2022. "Machine Learning-Based Load Forecasting for Nanogrid Peak Load Cost Reduction," Energies, MDPI, vol. 15(18), pages 1-23, September.
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    24. Shi, Jiaqi & Li, Chenxi & Yan, Xiaohe, 2023. "Artificial intelligence for load forecasting: A stacking learning approach based on ensemble diversity regularization," Energy, Elsevier, vol. 262(PB).

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