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A Hybrid Channel-Communication-Enabled CNN-LSTM Model for Electricity Load Forecasting

Author

Listed:
  • Faisal Saeed

    (Department of Computer Science and Engineering, Kyungpook National University, Buk-gu, Daegu 41566, Korea)

  • Anand Paul

    (Department of Computer Science and Engineering, Kyungpook National University, Buk-gu, Daegu 41566, Korea)

  • Hyuncheol Seo

    (School of Architectural, Civil, Environmental and Energy Engineering, Kyungpook National University, Daegu 41566, Korea)

Abstract

Smart grids provide a unique platform to the participants of energy markets to tweak their offerings based on demand-side management. Responding quickly to the needs of the market can help to improve the reliability of the system, as well as the cost of capital investments. Electric load forecasting is important because it is used to make and run decisions about the power grid. However, people use electricity in nonlinear ways, which makes the electric load profile a complicated signal. Even though there has been a lot of research done in this field, an accurate forecasting model is still needed. In this regard, this article proposed a hybrid cross-channel-communication (C3)-enabled CNN-LSTM model for accurate load forecasting which helps decision making in smart grids. The proposed model is the combination of three different models, i.e., a C3 block to enable channel communication of a CNN (convolutional neural networks) model, two convolutional layers to extract the features and an LSTM (long short-term memory network) model for forecasting. In the proposed hybrid model, Leaky ReLu (rectified linear unit) was used as activation function instead of sigmoid. The channel communication in CNN model makes the proposed model very light and efficient. Extensive experimentation was done on electricity load data. The results show the model’s high efficiency. The proposed model shows 98.3% accuracy and 0.4560 MAPE error.

Suggested Citation

  • Faisal Saeed & Anand Paul & Hyuncheol Seo, 2022. "A Hybrid Channel-Communication-Enabled CNN-LSTM Model for Electricity Load Forecasting," Energies, MDPI, vol. 15(6), pages 1-17, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:2263-:d:775314
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    References listed on IDEAS

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    1. Yu, Mengmeng & Hong, Seung Ho, 2016. "Supply–demand balancing for power management in smart grid: A Stackelberg game approach," Applied Energy, Elsevier, vol. 164(C), pages 702-710.
    2. Rahman, Aowabin & Srikumar, Vivek & Smith, Amanda D., 2018. "Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks," Applied Energy, Elsevier, vol. 212(C), pages 372-385.
    3. Amjady, N. & Keynia, F., 2009. "Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm," Energy, Elsevier, vol. 34(1), pages 46-57.
    4. Li, Song & Goel, Lalit & Wang, Peng, 2016. "An ensemble approach for short-term load forecasting by extreme learning machine," Applied Energy, Elsevier, vol. 170(C), pages 22-29.
    5. Colak, Ilhami & Fulli, Gianluca & Sagiroglu, Seref & Yesilbudak, Mehmet & Covrig, Catalin-Felix, 2015. "Smart grid projects in Europe: Current status, maturity and future scenarios," Applied Energy, Elsevier, vol. 152(C), pages 58-70.
    6. Jian, Linni & Zheng, Yanchong & Xiao, Xinping & Chan, C.C., 2015. "Optimal scheduling for vehicle-to-grid operation with stochastic connection of plug-in electric vehicles to smart grid," Applied Energy, Elsevier, vol. 146(C), pages 150-161.
    7. Magnus Dahl & Adam Brun & Oliver S. Kirsebom & Gorm B. Andresen, 2018. "Improving Short-Term Heat Load Forecasts with Calendar and Holiday Data," Energies, MDPI, vol. 11(7), pages 1-16, June.
    8. 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).
    9. Zhang, Jinliang & Wei, Yi-Ming & Li, Dezhi & Tan, Zhongfu & Zhou, Jianhua, 2018. "Short term electricity load forecasting using a hybrid model," Energy, Elsevier, vol. 158(C), pages 774-781.
    10. Xiao, Liye & Shao, Wei & Wang, Chen & Zhang, Kequan & Lu, Haiyan, 2016. "Research and application of a hybrid model based on multi-objective optimization for electrical load forecasting," Applied Energy, Elsevier, vol. 180(C), pages 213-233.
    11. Yang, Yandong & Hong, Weijun & Li, Shufang, 2019. "Deep ensemble learning based probabilistic load forecasting in smart grids," Energy, Elsevier, vol. 189(C).
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