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An Ensemble Framework for Short-Term Load Forecasting Based on TimesNet and TCN

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  • Chuanhui Zuo

    (School of Information Engineering, Nanchang University, Nanchang 330031, China)

  • Jialong Wang

    (School of Information Engineering, Nanchang University, Nanchang 330031, China)

  • Mingping Liu

    (School of Information Engineering, Nanchang University, Nanchang 330031, China)

  • Suhui Deng

    (School of Information Engineering, Nanchang University, Nanchang 330031, China)

  • Qingnian Wang

    (School of Information Engineering, Nanchang University, Nanchang 330031, China)

Abstract

Accurate and efficient short-term power load forecasting is crucial for ensuring the stable operation of power systems and rational planning of electricity resources. However, power load data are often characterized by nonlinearity and instability due to external factors such as meteorological conditions and day types, making accurate load forecasting challenging. While some hybrid models can effectively capture the spatiotemporal features of power load data, they often overlook the multi-periodicity of load data, leading to suboptimal feature extraction and efficiency. In this paper, a novel hybrid framework for short-term load forecasting based on TimesNet and temporal convolutional network (TCN) is proposed. Firstly, the original load data are preprocessed to reconstruct a feature matrix. Secondly, the TimesNet transforms the one-dimensional time series into a set of two-dimensional tensors based on multiple periods, capturing dependencies within different time scales and the relationships between different time scales in power load data. Then, the temporal convolutional network is employed to further extract the temporal features and long-term dependencies of the load data, enabling a more global pattern to be obtained for temporal information. Finally, the results of load forecasting can be achieved from the fully connected layer based on the extracted features. To verify the effectiveness and generalization of the proposed model, experiments have been conducted based on the ISO-NE and Southern China datasets. Experimental results show that the proposed model greatly outperforms the long short-term memory (LSTM), TCN, TimesNet, TCN-LSTM, and TimesNet-LSTM models. The proposed model reduces the mean absolute percentage error by 20% to 43% for the ISO-NE dataset and by 10% to 31% for the Southern China dataset, respectively.

Suggested Citation

  • Chuanhui Zuo & Jialong Wang & Mingping Liu & Suhui Deng & Qingnian Wang, 2023. "An Ensemble Framework for Short-Term Load Forecasting Based on TimesNet and TCN," Energies, MDPI, vol. 16(14), pages 1-17, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:14:p:5330-:d:1192300
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    References listed on IDEAS

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    1. Kim, Tae-Young & Cho, Sung-Bae, 2019. "Predicting residential energy consumption using CNN-LSTM neural networks," Energy, Elsevier, vol. 182(C), pages 72-81.
    2. Mingping Liu & Xihao Sun & Qingnian Wang & Suhui Deng, 2022. "Short-Term Load Forecasting Using EMD with Feature Selection and TCN-Based Deep Learning Model," Energies, MDPI, vol. 15(19), pages 1-22, September.
    3. Weide Li & Xuan Yang & Hao Li & Lili Su, 2017. "Hybrid Forecasting Approach Based on GRNN Neural Network and SVR Machine for Electricity Demand Forecasting," Energies, MDPI, vol. 10(1), pages 1-17, January.
    4. Jemma J. Makrygiorgou & Christos-Spyridon Karavas & Christos Dikaiakos & Ioannis P. Moraitis, 2023. "The Electricity Market in Greece: Current Status, Identified Challenges, and Arranged Reforms," Sustainability, MDPI, vol. 15(4), pages 1-40, February.
    5. 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.
    6. Weili Cai & Wenjuan Zhang & Xiaofeng Hu & Yingchao Liu, 2020. "A hybrid information model based on long short-term memory network for tool condition monitoring," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1497-1510, August.
    7. Hafeez, Ghulam & Alimgeer, Khurram Saleem & Khan, Imran, 2020. "Electric load forecasting based on deep learning and optimized by heuristic algorithm in smart grid," Applied Energy, Elsevier, vol. 269(C).
    8. 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.
    9. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
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