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A hybrid model based on modified multi-objective cuckoo search algorithm for short-term load forecasting

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  • Wu, Zhuochun
  • Zhao, Xiaochen
  • Ma, Yuqing
  • Zhao, Xinyan

Abstract

To ensure the safe operation of electrical power systems, short-term load forecasting (STLF) plays a significant role. With the development of artificial neural network (ANN), many forecasting models based on ANN are proposed to enhance the forecasting accuracy. However, forecasting stability is also an important aspect when considering a forecasting model. Both forecasting accuracy and stability are affected heavily by the random initial values of weights and thresholds of ANN. Thus, in this paper, a new hybrid model based on the modified generalized regression neural network (GRNN) is proposed for short-term load forecasting (STLF). Meanwhile, a non-dominated sorting-based multi-objective cuckoo search algorithm (NSMOCS) is proposed to realize accurate and stable forecasting simultaneously. To utilize the similarities and reduce interference existing in the original data, some data pre-processing techniques are also incorporated. With half-hourly load data from five states in Australia, experimental results clearly show that the proposed hybrid model could obtain more accurate and stable forecasting results, compared with the comparison models.

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  • Wu, Zhuochun & Zhao, Xiaochen & Ma, Yuqing & Zhao, Xinyan, 2019. "A hybrid model based on modified multi-objective cuckoo search algorithm for short-term load forecasting," Applied Energy, Elsevier, vol. 237(C), pages 896-909.
    • Handle: RePEc:eee:appene:v:237:y:2019:i:c:p:896-909
    DOI: 10.1016/j.apenergy.2019.01.046
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    Cited by:

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    2. Ribeiro, Matheus Henrique Dal Molin & da Silva, Ramon Gomes & Ribeiro, Gabriel Trierweiler & Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2023. "Cooperative ensemble learning model improves electric short-term load forecasting," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
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    6. Hafeez, Ghulam & Alimgeer, Khurram Saleem & Khan, Imran, 2020. "Electric load forecasting based on deep learning and optimized by heuristic algorithm in smart grid," Applied Energy, Elsevier, vol. 269(C).
    7. 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).
    8. Li, Yiyan & Zhang, Si & Hu, Rongxing & Lu, Ning, 2021. "A meta-learning based distribution system load forecasting model selection framework," Applied Energy, Elsevier, vol. 294(C).
    9. Yelin Wang & Ping Yang & Zan Song & Julien Chevallier & Qingtai Xiao, 2024. "Intelligent Prediction of Annual CO2 Emissions Under Data Decomposition Mode," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 711-740, February.
    10. Luo, X.J. & Oyedele, Lukumon O. & Ajayi, Anuoluwapo O. & Akinade, Olugbenga O. & Owolabi, Hakeem A. & Ahmed, Ashraf, 2020. "Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    11. Laouafi, Abderrezak & Laouafi, Farida & Boukelia, Taqiy Eddine, 2022. "An adaptive hybrid ensemble with pattern similarity analysis and error correction for short-term load forecasting," Applied Energy, Elsevier, vol. 322(C).
    12. Bashiri Behmiri, Niaz & Fezzi, Carlo & Ravazzolo, Francesco, 2023. "Incorporating air temperature into mid-term electricity load forecasting models using time-series regressions and neural networks," Energy, Elsevier, vol. 278(C).
    13. Zengping Wang & Bing Zhao & Haibo Guo & Lingling Tang & Yuexing Peng, 2019. "Deep Ensemble Learning Model for Short-Term Load Forecasting within Active Learning Framework," Energies, MDPI, vol. 12(20), pages 1-13, October.
    14. Jindong Yang & Xiran Zhang & Wenhao Chen & Fei Rong, 2024. "Prophet–CEEMDAN–ARBiLSTM-Based Model for Short-Term Load Forecasting," Future Internet, MDPI, vol. 16(6), pages 1-16, May.
    15. Zifa Liu & Xinyi Li & Haiyan Zhao, 2023. "Short-Term Wind Power Forecasting Based on Feature Analysis and Error Correction," Energies, MDPI, vol. 16(10), pages 1-24, May.
    16. Niu, Dongxiao & Ji, Zhengsen & Li, Wanying & Xu, Xiaomin & Liu, Da, 2021. "Research and application of a hybrid model for mid-term power demand forecasting based on secondary decomposition and interval optimization," Energy, Elsevier, vol. 234(C).
    17. Somu, Nivethitha & M R, Gauthama Raman & Ramamritham, Krithi, 2020. "A hybrid model for building energy consumption forecasting using long short term memory networks," Applied Energy, Elsevier, vol. 261(C).
    18. Sun, Zexian & Zhao, Mingyu & Zhao, Guohong, 2022. "Hybrid model based on VMD decomposition, clustering analysis, long short memory network, ensemble learning and error complementation for short-term wind speed forecasting assisted by Flink platform," Energy, Elsevier, vol. 261(PB).
    19. Thomas Mobius & Mira Watermeyer & Oliver Grothe & Felix Musgens, 2023. "Enhancing Energy System Models Using Better Load Forecasts," Papers 2302.11017, arXiv.org.
    20. Giancarlo Aquila & Lucas Barros Scianni Morais & Victor Augusto Durães de Faria & José Wanderley Marangon Lima & Luana Medeiros Marangon Lima & Anderson Rodrigo de Queiroz, 2023. "An Overview of Short-Term Load Forecasting for Electricity Systems Operational Planning: Machine Learning Methods and the Brazilian Experience," Energies, MDPI, vol. 16(21), pages 1-35, November.
    21. Wu, Zhuochun & Xiao, Liye, 2019. "A structure with density-weighted active learning-based model selection strategy and meteorological analysis for wind speed vector deterministic and probabilistic forecasting," Energy, Elsevier, vol. 183(C), pages 1178-1194.
    22. Hu, Yi & Qu, Boyang & Wang, Jie & Liang, Jing & Wang, Yanli & Yu, Kunjie & Li, Yaxin & Qiao, Kangjia, 2021. "Short-term load forecasting using multimodal evolutionary algorithm and random vector functional link network based ensemble learning," Applied Energy, Elsevier, vol. 285(C).
    23. Meng, Anbo & Zhu, Zibin & Deng, Weisi & Ou, Zuhong & Lin, Shan & Wang, Chenen & Xu, Xuancong & Wang, Xiaolin & Yin, Hao & Luo, Jianqiang, 2022. "A novel wind power prediction approach using multivariate variational mode decomposition and multi-objective crisscross optimization based deep extreme learning machine," Energy, Elsevier, vol. 260(C).

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