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Enhanced Short-Term Load Forecasting Using Artificial Neural Networks

Author

Listed:
  • Athanasios Ioannis Arvanitidis

    (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)

  • Vasileios M. Laitsos

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

  • Lefteri H. Tsoukalas

    (School of Nuclear Engineering, Purdue University, West Lafayette, IN 47907, USA)

Abstract

The modernization and optimization of current power systems are the objectives of research and development in the energy sector, which is motivated by the ever-increasing electricity demands. The goal of such research and development is to render power electronic equipment more controllable, to ensure maximal use of current circuits, system flexibility and efficiency, as well as the relatively easy integration of renewable energy resources at all voltage levels. The current revolution in communication technologies and the Internet of Things (IoT) offers us an opportunity to supervise and regulate the power grid, in order to achieve more reliable, efficient, and cost-effective services. One of the most critical aspects of efficient power system operation is the ability to predict energy load requirements, i.e., load forecasting. Load forecasting is essential for balancing demand and supply and for determining electricity prices. Typically, load forecasting has been supported through the use of Artificial Neural Networks (ANNs), which, once trained on a set of data, can predict future loads. The accuracy of the ANNs’ prediction depends on the quality and availability of the training data. In this paper, we propose novel data pre-processing strategies, which we apply to the data used to train an ANN, and subsequently evaluate the quality of the predictions it produces, to demonstrate the benefits gained. The proposed strategies and the obtained results are illustrated using consumption data from the Greek interconnected power system.

Suggested Citation

  • Athanasios Ioannis Arvanitidis & Dimitrios Bargiotas & Aspassia Daskalopulu & Vasileios M. Laitsos & Lefteri H. Tsoukalas, 2021. "Enhanced Short-Term Load Forecasting Using Artificial Neural Networks," Energies, MDPI, vol. 14(22), pages 1-14, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:22:p:7788-:d:684225
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    References listed on IDEAS

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    Cited by:

    1. Ahmed Mazin Majid AL-Qaysi & Altug Bozkurt & Yavuz Ates, 2023. "Load Forecasting Based on Genetic Algorithm–Artificial Neural Network-Adaptive Neuro-Fuzzy Inference Systems: A Case Study in Iraq," Energies, MDPI, vol. 16(6), pages 1-20, March.
    2. Sivakavi Naga Venkata Bramareswara Rao & Venkata Pavan Kumar Yellapragada & Kottala Padma & Darsy John Pradeep & Challa Pradeep Reddy & Mohammad Amir & Shady S. Refaat, 2022. "Day-Ahead Load Demand Forecasting in Urban Community Cluster Microgrids Using Machine Learning Methods," Energies, MDPI, vol. 15(17), pages 1-25, August.
    3. 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.
    4. Roman V. Klyuev & Irbek D. Morgoev & Angelika D. Morgoeva & Oksana A. Gavrina & Nikita V. Martyushev & Egor A. Efremenkov & Qi Mengxu, 2022. "Methods of Forecasting Electric Energy Consumption: A Literature Review," Energies, MDPI, vol. 15(23), pages 1-33, November.
    5. Shi, Jian & Teh, Jiashen & Alharbi, Bader & Lai, Ching-Ming, 2024. "Load forecasting for regional integrated energy system based on two-phase decomposition and mixture prediction model," Energy, Elsevier, vol. 297(C).
    6. Marta Moure-Garrido & Celeste Campo & Carlos Garcia-Rubio, 2022. "Entropy-Based Anomaly Detection in Household Electricity Consumption," Energies, MDPI, vol. 15(5), pages 1-21, March.
    7. Abdullah Alrasheedi & Abdulaziz Almalaq, 2022. "Hybrid Deep Learning Applied on Saudi Smart Grids for Short-Term Load Forecasting," Mathematics, MDPI, vol. 10(15), pages 1-22, July.
    8. Shi, Jian & Teh, Jiashen, 2024. "Load forecasting for regional integrated energy system based on complementary ensemble empirical mode decomposition and multi-model fusion," Applied Energy, Elsevier, vol. 353(PB).

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