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Short-term load forecasting with an improved dynamic decomposition-reconstruction-ensemble approach

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  • Yang, Dongchuan
  • Guo, Ju-e
  • Li, Yanzhao
  • Sun, Shaolong
  • Wang, Shouyang

Abstract

Short-term load forecasting has evolved into an important aspect of power system in safe operation and rational dispatching. However, given the load series' instability and volatility, this is a challenging task. To this end, this study proposes a dynamic decomposition-reconstruction-ensemble approach by cleverly and dynamically combining two proven and effective techniques (i.e., the reconstruction techniques and the secondary decomposition techniques). In fact, by introducing the decomposition-reconstruction process based on the dynamic classification, filtering, and giving the criteria for determining the components that need to be decomposed again, our proposed model improves the decomposition-ensemble forecasting framework. Our proposed model makes full use of decomposition techniques, complexity analysis, reconstruction techniques, secondary decomposition techniques, and a neural network optimized by an automatic hyperparameter optimization algorithm. Besides, we compared our proposed model with state-of-the-art models including five models with reconstruction techniques and two models with secondary decomposition techniques. The experiment results demonstrate the superiority of our proposed dynamic decomposition-reconstruction technique in terms of forecasting accuracy, precise direction, equality, stability, correlation, comprehensive accuracy, and statistical tests. To conclude, our proposed model has the potential to be a useful tool for short-term load forecasting.

Suggested Citation

  • Yang, Dongchuan & Guo, Ju-e & Li, Yanzhao & Sun, Shaolong & Wang, Shouyang, 2023. "Short-term load forecasting with an improved dynamic decomposition-reconstruction-ensemble approach," Energy, Elsevier, vol. 263(PA).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pa:s0360544222024951
    DOI: 10.1016/j.energy.2022.125609
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    as
    1. Mohan, Neethu & Soman, K.P. & Sachin Kumar, S., 2018. "A data-driven strategy for short-term electric load forecasting using dynamic mode decomposition model," Applied Energy, Elsevier, vol. 232(C), pages 229-244.
    2. Yu, Lean & Wang, Zishu & Tang, Ling, 2015. "A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting," Applied Energy, Elsevier, vol. 156(C), pages 251-267.
    3. Zhang, Xun & Lai, K.K. & Wang, Shou-Yang, 2008. "A new approach for crude oil price analysis based on Empirical Mode Decomposition," Energy Economics, Elsevier, vol. 30(3), pages 905-918, May.
    4. Li, Chen, 2020. "Designing a short-term load forecasting model in the urban smart grid system," Applied Energy, Elsevier, vol. 266(C).
    5. Jiménez-Cordero, Asunción & Morales, Juan Miguel & Pineda, Salvador, 2021. "A novel embedded min-max approach for feature selection in nonlinear Support Vector Machine classification," European Journal of Operational Research, Elsevier, vol. 293(1), pages 24-35.
    6. Niu, Hongli & Xu, Kunliang & Liu, Cheng, 2021. "A decomposition-ensemble model with regrouping method and attention-based gated recurrent unit network for energy price prediction," Energy, Elsevier, vol. 231(C).
    7. Hu, Yusha & Li, Jigeng & Hong, Mengna & Ren, Jingzheng & Lin, Ruojue & Liu, Yue & Liu, Mengru & Man, Yi, 2019. "Short term electric load forecasting model and its verification for process industrial enterprises based on hybrid GA-PSO-BPNN algorithm—A case study of papermaking process," Energy, Elsevier, vol. 170(C), pages 1215-1227.
    8. Sun, Wei & Huang, Chenchen, 2020. "A novel carbon price prediction model combines the secondary decomposition algorithm and the long short-term memory network," Energy, Elsevier, vol. 207(C).
    9. Bangzhu Zhu & Xuetao Shi & Julien Chevallier & Ping Wang & Yi‐Ming Wei, 2016. "An Adaptive Multiscale Ensemble Learning Paradigm for Nonstationary and Nonlinear Energy Price Time Series Forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(7), pages 633-651, November.
    10. Barman, Mayur & Dev Choudhury, Nalin Behari, 2019. "Season specific approach for short-term load forecasting based on hybrid FA-SVM and similarity concept," Energy, Elsevier, vol. 174(C), pages 886-896.
    11. Lebotsa, Moshoko Emily & Sigauke, Caston & Bere, Alphonce & Fildes, Robert & Boylan, John E., 2018. "Short term electricity demand forecasting using partially linear additive quantile regression with an application to the unit commitment problem," Applied Energy, Elsevier, vol. 222(C), pages 104-118.
    12. Tian, Chengshi & Hao, Yan & Hu, Jianming, 2018. "A novel wind speed forecasting system based on hybrid data preprocessing and multi-objective optimization," Applied Energy, Elsevier, vol. 231(C), pages 301-319.
    13. Żymełka, Piotr & Szega, Marcin, 2020. "Issues of an improving the accuracy of energy carriers production forecasting in a computer-aided system for monitoring the operation of a gas-fired cogeneration plant," Energy, Elsevier, vol. 209(C).
    14. Zengyuan Wu & Caihong Zhou & Fei Xu & Wengao Lou, 2022. "A CS-AdaBoost-BP model for product quality inspection," Annals of Operations Research, Springer, vol. 308(1), pages 685-701, January.
    15. Yang, Wendong & Wang, Jianzhou & Niu, Tong & Du, Pei, 2019. "A hybrid forecasting system based on a dual decomposition strategy and multi-objective optimization for electricity price forecasting," Applied Energy, Elsevier, vol. 235(C), pages 1205-1225.
    16. Song, Jingjing & Wang, Jianzhou & Lu, Haiyan, 2018. "A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 215(C), pages 643-658.
    17. Yang, Dongchuan & Guo, Ju-e & Sun, Shaolong & Han, Jing & Wang, Shouyang, 2022. "An interval decomposition-ensemble approach with data-characteristic-driven reconstruction for short-term load forecasting," Applied Energy, Elsevier, vol. 306(PA).
    18. Xiao, Ling & Wang, Jianzhou & Dong, Yao & Wu, Jie, 2015. "Combined forecasting models for wind energy forecasting: A case study in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 271-288.
    19. Li, Wei-Qin & Chang, Li, 2018. "A combination model with variable weight optimization for short-term electrical load forecasting," Energy, Elsevier, vol. 164(C), pages 575-593.
    20. Li, Der-Chiang & Chang, Che-Jung & Chen, Chien-Chih & Chen, Wen-Chih, 2012. "Forecasting short-term electricity consumption using the adaptive grey-based approach—An Asian case," Omega, Elsevier, vol. 40(6), pages 767-773.
    21. Duan, Jiandong & Wang, Peng & Ma, Wentao & Tian, Xuan & Fang, Shuai & Cheng, Yulin & Chang, Ying & Liu, Haofan, 2021. "Short-term wind power forecasting using the hybrid model of improved variational mode decomposition and Correntropy Long Short -term memory neural network," Energy, Elsevier, vol. 214(C).
    22. Xie, Gang & Qian, Yatong & Wang, Shouyang, 2020. "A decomposition-ensemble approach for tourism forecasting," Annals of Tourism Research, Elsevier, vol. 81(C).
    23. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    24. Xiaolei Sun & Jun Hao & Jianping Li, 2022. "Multi-objective optimization of crude oil-supply portfolio based on interval prediction data," Annals of Operations Research, Springer, vol. 309(2), pages 611-639, February.
    25. Sun, zexian & Zhao, mingyu & Dong, yan & Cao, xin & Sun, Hexu, 2021. "Hybrid model with secondary decomposition, randomforest algorithm, clustering analysis and long short memory network principal computing for short-term wind power forecasting on multiple scales," Energy, Elsevier, vol. 221(C).
    26. Guo, Zhifeng & Zhou, Kaile & Zhang, Xiaoling & Yang, Shanlin, 2018. "A deep learning model for short-term power load and probability density forecasting," Energy, Elsevier, vol. 160(C), pages 1186-1200.
    27. He, Feifei & Zhou, Jianzhong & Feng, Zhong-kai & Liu, Guangbiao & Yang, Yuqi, 2019. "A hybrid short-term load forecasting model based on variational mode decomposition and long short-term memory networks considering relevant factors with Bayesian optimization algorithm," Applied Energy, Elsevier, vol. 237(C), pages 103-116.
    28. Sun, Na & Zhou, Jianzhong & Chen, Lu & Jia, Benjun & Tayyab, Muhammad & Peng, Tian, 2018. "An adaptive dynamic short-term wind speed forecasting model using secondary decomposition and an improved regularized extreme learning machine," Energy, Elsevier, vol. 165(PB), pages 939-957.
    29. Li, Hongtao & Bai, Juncheng & Li, Yongwu, 2019. "A novel secondary decomposition learning paradigm with kernel extreme learning machine for multi-step forecasting of container throughput," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    30. Liu, Hui & Tian, Hong-qi & Liang, Xi-feng & Li, Yan-fei, 2015. "Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks," Applied Energy, Elsevier, vol. 157(C), pages 183-194.
    31. Ling Tang & Shuai Wang & Kaijian He & Shouyang Wang, 2015. "A novel mode-characteristic-based decomposition ensemble model for nuclear energy consumption forecasting," Annals of Operations Research, Springer, vol. 234(1), pages 111-132, November.
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