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Mid- to Long-Term Electric Load Forecasting Based on the EMD–Isomap–Adaboost Model

Author

Listed:
  • Xuguang Han

    (School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China)

  • Jingming Su

    (School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China)

  • Yan Hong

    (School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China)

  • Pingshun Gong

    (School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China)

  • Danping Zhu

    (School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China)

Abstract

Accurate load forecasting is an important issue for the reliable and efficient operation of a power system. In this study, a hybrid algorithm (EMDIA) that combines empirical mode decomposition (EMD), isometric mapping (Isomap), and Adaboost to construct a prediction mode for mid- to long-term load forecasting is developed. Based on full consideration of the meteorological and economic factors affecting the power load trend, the EMD method is used to decompose the load and its influencing factors into multiple intrinsic mode functions (IMF) and residuals. Through correlation analysis, the power load is divided into fluctuation term and trend term. Then, the key influencing factors of feature sequences are extracted by Isomap to eliminate the correlations and redundancy of the original multidimensional sequences and reduce the dimension of model input. Eventually, the Adaboost prediction method is adopted to realize the prediction of the electrical load. In comparison with the RF, LSTM, GRU, BP, and single Adaboost method, the prediction obtained by this proposed model has higher accuracy in the mean absolute percentage error (MAPE), mean absolute error (MAE), root mean square error (RMSE), and determination coefficient (R 2 ). Compared with the single Adaboost algorithm, the EMDIA reduces MAE by 11.58, MAPE by 0.13%, and RMSE by 49.93 and increases R 2 by 0.04.

Suggested Citation

  • Xuguang Han & Jingming Su & Yan Hong & Pingshun Gong & Danping Zhu, 2022. "Mid- to Long-Term Electric Load Forecasting Based on the EMD–Isomap–Adaboost Model," Sustainability, MDPI, vol. 14(13), pages 1-15, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:7608-:d:845063
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    References listed on IDEAS

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    1. Jing Yu & Feng Ding & Chenghao Guo & Yabin Wang, 2019. "System load trend prediction method based on IF-EMD-LSTM," International Journal of Distributed Sensor Networks, , vol. 15(8), pages 15501477198, August.
    2. Masoud Sobhani & Allison Campbell & Saurabh Sangamwar & Changlin Li & Tao Hong, 2019. "Combining Weather Stations for Electric Load Forecasting," Energies, MDPI, vol. 12(8), pages 1-11, April.
    3. Lindberg, K.B. & Seljom, P. & Madsen, H. & Fischer, D. & Korpås, M., 2019. "Long-term electricity load forecasting: Current and future trends," Utilities Policy, Elsevier, vol. 58(C), pages 102-119.
    4. Hong, Wei-Chiang, 2011. "Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm," Energy, Elsevier, vol. 36(9), pages 5568-5578.
    5. Page Kyle & Leon Clarke & Fang Rong & Steven J. Smith, 2010. "Climate Policy and the Long-Term Evolution of the U.S. Buildings Sector," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 145-172.
    6. Xin Gao & Xiaobing Li & Bing Zhao & Weijia Ji & Xiao Jing & Yang He, 2019. "Short-Term Electricity Load Forecasting Model Based on EMD-GRU with Feature Selection," Energies, MDPI, vol. 12(6), pages 1-18, March.
    7. Tawn, R. & Browell, J., 2022. "A review of very short-term wind and solar power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    8. Liu, Qingchao & Cai, Yingfeng & Jiang, Haobin & Lu, Jian & Chen, Long, 2018. "Traffic state prediction using ISOMAP manifold learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 532-541.
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