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Short-Term Campus Load Forecasting Using CNN-Based Encoder–Decoder Network with Attention

Author

Listed:
  • Zain Ahmed

    (Department of Electrical and Computer Engineering, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, Canada)

  • Mohsin Jamil

    (Department of Electrical and Computer Engineering, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, Canada)

  • Ashraf Ali Khan

    (Department of Electrical and Computer Engineering, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, Canada)

Abstract

Short-term load forecasting is a challenging research problem and has a tremendous impact on electricity generation, transmission, and distribution. A robust forecasting algorithm can help power system operators to better tackle the ever-changing electric power demand. This paper presents a novel deep neural network for short-term electric load forecasting for the St. John’s campus of Memorial University of Newfoundland (MUN). The electric load data are obtained from the Memorial University of Newfoundland and combined with metrological data from St. John’s. This dataset is used to formulate a multivariate time-series forecasting problem. A novel deep learning algorithm is presented, consisting of a 1D Convolutional Neural Network, which is followed by an encoder–decoder-based network with attention. The input used for this model is the electric load consumption and metrological data, while the output is the hourly prediction of the next day. The model is compared with Gated Recurrent Unit (GRU) and Long Short Term Memory (LSTM)-based Recurrent Neural Network. A CNN-based encoder–decoder model without attention is also tested. The proposed model shows a lower mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and higher R 2 score. These evaluation metrics show an improved performance compared to GRU and LSTM-based RNNs as well as the CNN encoder–decoder model without attention.

Suggested Citation

  • Zain Ahmed & Mohsin Jamil & Ashraf Ali Khan, 2024. "Short-Term Campus Load Forecasting Using CNN-Based Encoder–Decoder Network with Attention," Energies, MDPI, vol. 17(17), pages 1-19, September.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:17:p:4457-:d:1471916
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    References listed on IDEAS

    as
    1. Zhang, Wenyu & Chen, Qian & Yan, Jianyong & Zhang, Shuai & Xu, Jiyuan, 2021. "A novel asynchronous deep reinforcement learning model with adaptive early forecasting method and reward incentive mechanism for short-term load forecasting," Energy, Elsevier, vol. 236(C).
    2. 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.
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