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Clustering Informed MLP Models for Fast and Accurate Short-Term Load Forecasting

Author

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  • 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)

  • Dimitrios Kontogiannis

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

  • Ioannis P. Panapakidis

    (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 47907, USA)

Abstract

The stable and efficient operation of power systems requires them to be optimized, which, given the growing availability of load data, relies on load forecasting methods. Fast and highly accurate Short-Term Load Forecasting (STLF) is critical for the daily operation of power plants, and state-of-the-art approaches for it involve hybrid models that deploy regressive deep learning algorithms, such as neural networks, in conjunction with clustering techniques for the pre-processing of load data before they are fed to the neural network. This paper develops and evaluates four robust STLF models based on Multi-Layer Perceptrons (MLPs) coupled with the K-Means and Fuzzy C-Means clustering algorithms. The first set of two models cluster the data before feeding it to the MLPs, and are directly comparable to similar existing approaches, yielding, however, better forecasting accuracy. They also serve as a common reference point for the evaluation of the second set of two models, which further enhance the input to the MLP by informing it explicitly with clustering information, which is a novel feature. All four models are designed, tested and evaluated using data from the Greek power system, although their development is generic and they could, in principle, be applied to any power system. The results obtained by the four models are compared to those of other STLF methods, using objective metrics, and the accuracy obtained, as well as convergence time, is in most cases improved.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1295-:d:746504
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    References listed on IDEAS

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    4. 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.
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    Cited by:

    1. João Victor Jales Melo & George Rossany Soares Lira & Edson Guedes Costa & Antonio F. Leite Neto & Iago B. Oliveira, 2022. "Short-Term Load Forecasting on Individual Consumers," Energies, MDPI, vol. 15(16), pages 1-16, August.
    2. Max Olinto Moreira & Betania Mafra Kaizer & Takaaki Ohishi & Benedito Donizeti Bonatto & Antonio Carlos Zambroni de Souza & Pedro Paulo Balestrassi, 2022. "Multivariate Strategy Using Artificial Neural Networks for Seasonal Photovoltaic Generation Forecasting," Energies, MDPI, vol. 16(1), pages 1-30, December.
    3. 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.

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