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An adaptive ensemble framework with representative subset based weight correction for short-term forecast of peak power load

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  • Che, Jinxing
  • Yuan, Fang
  • Zhu, Suling
  • Yang, Youlong

Abstract

In the ensemble learning-based load prediction systems, recent research studies have achieved remarkable results in terms of extracting the complementary merits of multiple individual models and reducing the forecasting uncertainty, but they lack appropriate guidance for the sample-specific ensemble weighting of actual load data showing dynamics and variability. To this end, this study proposes an adaptive ensemble framework to address the above challenging and still tricky problem, and an adaptive weight-based ensemble learning model (AW-ELM) is framed to forecast short-term electric load by introducing the representative subset based weight correction with respect to the data characteristics. In the model, a weight library is formed as representatives extracting from the given training data. Based on the representatives, probability distribution modeling is built via a distance method. Then, the adaptive weights of the test data are determined by using the best matching concept. Experiments of two electric power companies in Jiangxi Province, China are implemented to evaluate the performance of the ensemble model. The results show that the ensemble model achieves better forecasting accuracy than the average combined forecasting model and the comparison models.

Suggested Citation

  • Che, Jinxing & Yuan, Fang & Zhu, Suling & Yang, Youlong, 2022. "An adaptive ensemble framework with representative subset based weight correction for short-term forecast of peak power load," Applied Energy, Elsevier, vol. 328(C).
    • Handle: RePEc:eee:appene:v:328:y:2022:i:c:s0306261922014131
    DOI: 10.1016/j.apenergy.2022.120156
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    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. Chahkoutahi, Fatemeh & Khashei, Mehdi, 2017. "A seasonal direct optimal hybrid model of computational intelligence and soft computing techniques for electricity load forecasting," Energy, Elsevier, vol. 140(P1), pages 988-1004.
    3. Hong, Tao & Pinson, Pierre & Fan, Shu, 2014. "Global Energy Forecasting Competition 2012," International Journal of Forecasting, Elsevier, vol. 30(2), pages 357-363.
    4. Xian, Huafeng & Che, Jinxing, 2022. "Multi-space collaboration framework based optimal model selection for power load forecasting," Applied Energy, Elsevier, vol. 314(C).
    5. Jeong, Dongyeon & Park, Chiwoo & Ko, Young Myoung, 2021. "Short-term electric load forecasting for buildings using logistic mixture vector autoregressive model with curve registration," Applied Energy, Elsevier, vol. 282(PB).
    6. Li, Lechen & Meinrenken, Christoph J. & Modi, Vijay & Culligan, Patricia J., 2021. "Short-term apartment-level load forecasting using a modified neural network with selected auto-regressive features," Applied Energy, Elsevier, vol. 287(C).
    7. Wang, Shaomin & Wang, Shouxiang & Chen, Haiwen & Gu, Qiang, 2020. "Multi-energy load forecasting for regional integrated energy systems considering temporal dynamic and coupling characteristics," Energy, Elsevier, vol. 195(C).
    8. Zhao, Weigang & Wei, Yi-Ming & Su, Zhongyue, 2016. "One day ahead wind speed forecasting: A resampling-based approach," Applied Energy, Elsevier, vol. 178(C), pages 886-901.
    9. 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).
    10. Kamyab, Farhad & Bahrami, Shahab, 2016. "Efficient operation of energy hubs in time-of-use and dynamic pricing electricity markets," Energy, Elsevier, vol. 106(C), pages 343-355.
    11. Hernández, Luis & Baladrón, Carlos & Aguiar, Javier M. & Carro, Belén & Sánchez-Esguevillas, Antonio & Lloret, Jaime, 2014. "Artificial neural networks for short-term load forecasting in microgrids environment," Energy, Elsevier, vol. 75(C), pages 252-264.
    12. Amato, Umberto & Antoniadis, Anestis & De Feis, Italia & Goude, Yannig & Lagache, Audrey, 2021. "Forecasting high resolution electricity demand data with additive models including smooth and jagged components," International Journal of Forecasting, Elsevier, vol. 37(1), pages 171-185.
    13. Xiao, Liye & Shao, Wei & Liang, Tulu & Wang, Chen, 2016. "A combined model based on multiple seasonal patterns and modified firefly algorithm for electrical load forecasting," Applied Energy, Elsevier, vol. 167(C), pages 135-153.
    14. 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.
    15. Hafeez, Ghulam & Khan, Imran & Jan, Sadaqat & Shah, Ibrar Ali & Khan, Farrukh Aslam & Derhab, Abdelouahid, 2021. "A novel hybrid load forecasting framework with intelligent feature engineering and optimization algorithm in smart grid," Applied Energy, Elsevier, vol. 299(C).
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