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Enhanced Automated Deep Learning Application for Short-Term Load Forecasting

Author

Listed:
  • Vasileios Laitsos

    (Department of Electrical and Computer Engineering, University of Thessaly, 38334 Volos, Greece)

  • Georgios Vontzos

    (Department of Electrical and Computer Engineering, University of Thessaly, 38334 Volos, Greece)

  • Dimitrios Bargiotas

    (Department of Electrical and Computer Engineering, University of Thessaly, 38334 Volos, Greece)

  • Aspassia Daskalopulu

    (Department of Electrical and Computer Engineering, University of Thessaly, 38334 Volos, Greece)

  • Lefteri H. Tsoukalas

    (Center for Intelligent Energy Systems (CiENS), School of Nuclear Engineering, Purdue University, West Lafayette, IN 47906, USA)

Abstract

In recent times, the power sector has become a focal point of extensive scientific interest, driven by a convergence of factors, such as mounting global concerns surrounding climate change, the persistent increase in electricity prices within the wholesale energy market, and the surge in investments catalyzed by technological advancements across diverse sectors. These evolving challenges have necessitated the emergence of new imperatives aimed at effectively managing energy resources, ensuring grid stability, bolstering reliability, and making informed decisions. One area that has garnered particular attention is the accurate prediction of end-user electricity load, which has emerged as a critical facet in the pursuit of efficient energy management. To tackle this challenge, machine and deep learning models have emerged as popular and promising approaches, owing to their having remarkable effectiveness in handling complex time series data. In this paper, the development of an algorithmic model that leverages an automated process to provide highly accurate predictions of electricity load, specifically tailored for the island of Thira in Greece, is introduced. Through the implementation of an automated application, an array of deep learning forecasting models were meticulously crafted, encompassing the Multilayer Perceptron, Long Short-Term Memory (LSTM), One Dimensional Convolutional Neural Network (CNN-1D), hybrid CNN–LSTM, Temporal Convolutional Network (TCN), and an innovative hybrid model called the Convolutional LSTM Encoder–Decoder. Through evaluation of prediction accuracy, satisfactory performance across all the models considered was observed, with the proposed hybrid model showcasing the highest level of accuracy. These findings underscore the profound significance of employing deep learning techniques for precise forecasting of electricity demand, thereby offering valuable insights with which to tackle the multifaceted challenges encountered within the power sector. By adopting advanced forecasting methodologies, the electricity sector moves towards greater efficiency, resilience and sustainability.

Suggested Citation

  • Vasileios Laitsos & Georgios Vontzos & Dimitrios Bargiotas & Aspassia Daskalopulu & Lefteri H. Tsoukalas, 2023. "Enhanced Automated Deep Learning Application for Short-Term Load Forecasting," Mathematics, MDPI, vol. 11(13), pages 1-21, June.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:13:p:2912-:d:1182274
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    References listed on IDEAS

    as
    1. Vasileios M. Laitsos & Dimitrios Bargiotas & Aspassia Daskalopulu & Athanasios Ioannis Arvanitidis & Lefteri H. Tsoukalas, 2021. "An Incentive-Based Implementation of Demand Side Management in Power Systems," Energies, MDPI, vol. 14(23), pages 1-24, November.
    2. Chujie Tian & Jian Ma & Chunhong Zhang & Panpan Zhan, 2018. "A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network," Energies, MDPI, vol. 11(12), pages 1-13, December.
    3. Dimitrios Kontogiannis & Dimitrios Bargiotas & Aspassia Daskalopulu, 2020. "Minutely Active Power Forecasting Models Using Neural Networks," Sustainability, MDPI, vol. 12(8), pages 1-17, April.
    4. Yu Jin & Honggang Guo & Jianzhou Wang & Aiyi Song, 2020. "A Hybrid System Based on LSTM for Short-Term Power Load Forecasting," Energies, MDPI, vol. 13(23), pages 1-32, November.
    5. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
    6. Huiting Zheng & Jiabin Yuan & Long Chen, 2017. "Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation," Energies, MDPI, vol. 10(8), pages 1-20, August.
    7. Athanasios Ioannis Arvanitidis & Dimitrios Bargiotas & Aspassia Daskalopulu & Dimitrios Kontogiannis & Ioannis P. Panapakidis & Lefteri H. Tsoukalas, 2022. "Clustering Informed MLP Models for Fast and Accurate Short-Term Load Forecasting," Energies, MDPI, vol. 15(4), pages 1-14, February.
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