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Short-term load forecast using ensemble neuro-fuzzy model

Author

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  • Malekizadeh, M.
  • Karami, H.
  • Karimi, M.
  • Moshari, A.
  • Sanjari, M.J.

Abstract

In this paper, Takagi-Sugeno-Kang neuro-fuzzy model is trained using locally linear model tree (LOLIMOT) method to forecast day-ahead hourly load profile. The proposed approach is applied to a real load profile measured in Iran as a geographically spread case study. The effects of partitioning the power system to smaller regions on the load forecasting and its advantages, such as practical consideration of daily average temperature data, are also shown. Moreover, a set of preprocessing approaches is proposed and implemented on historical load data to improve forecasting results. It is shown that by using LOLIMOT, the neuro-fuzzy model does not need the predetermined settings, such as the number of neurons, membership functions or fuzzy rules by an expert because all the parameters are set by the LOLIMOT method. This approach leads to the flexible network topology of the trained model for different days, which leads to extract the load profile trends more effectively.

Suggested Citation

  • Malekizadeh, M. & Karami, H. & Karimi, M. & Moshari, A. & Sanjari, M.J., 2020. "Short-term load forecast using ensemble neuro-fuzzy model," Energy, Elsevier, vol. 196(C).
    • Handle: RePEc:eee:energy:v:196:y:2020:i:c:s0360544220302346
    DOI: 10.1016/j.energy.2020.117127
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    References listed on IDEAS

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    Citations

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    Cited by:

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    2. Qu, Zhijian & Xu, Juan & Wang, Zixiao & Chi, Rui & Liu, Hanxin, 2021. "Prediction of electricity generation from a combined cycle power plant based on a stacking ensemble and its hyperparameter optimization with a grid-search method," Energy, Elsevier, vol. 227(C).
    3. Haoran Zhao & Sen Guo, 2021. "Uncertain Interval Forecasting for Combined Electricity-Heat-Cooling-Gas Loads in the Integrated Energy System Based on Multi-Task Learning and Multi-Kernel Extreme Learning Machine," Mathematics, MDPI, vol. 9(14), pages 1-32, July.
    4. Wang, Kang & Wang, Jianzhou & Zeng, Bo & Lu, Haiyan, 2022. "An integrated power load point-interval forecasting system based on information entropy and multi-objective optimization," Applied Energy, Elsevier, vol. 314(C).
    5. Dong, Ming & Shi, Jian & Shi, Qingxin, 2020. "Multi-year long-term load forecast for area distribution feeders based on selective sequence learning," Energy, Elsevier, vol. 206(C).
    6. Qingsheng Zhao & Juwen Mu & Xiaoqing Han & Dingkang Liang & Xuping Wang, 2021. "Evaluation Model of Operation State Based on Deep Learning for Smart Meter," Energies, MDPI, vol. 14(15), pages 1-17, August.
    7. 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).
    8. Sarah Hadri & Mehdi Najib & Mohamed Bakhouya & Youssef Fakhri & Mohamed El Arroussi, 2021. "Performance Evaluation of Forecasting Strategies for Electricity Consumption in Buildings," Energies, MDPI, vol. 14(18), pages 1-17, September.
    9. Tavassoli-Hojati, Z. & Ghaderi, S.F. & Iranmanesh, H. & Hilber, P. & Shayesteh, E., 2020. "A self-partitioning local neuro fuzzy model for short-term load forecasting in smart grids," Energy, Elsevier, vol. 199(C).
    10. Ibrahim Salem Jahan & Vaclav Snasel & Stanislav Misak, 2020. "Intelligent Systems for Power Load Forecasting: A Study Review," Energies, MDPI, vol. 13(22), pages 1-12, November.

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