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Application of Artificial Neural Networks for Natural Gas Consumption Forecasting

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  • Athanasios Anagnostis

    (Institute for Bio-economy and Agri-technology (iBO), Center for Research and Technology—Hellas (CERTH), Thessaloniki GR57001, Greece
    Department of Computer Science, University of Thessaly, Lamia GR35131, Greece)

  • Elpiniki Papageorgiou

    (Institute for Bio-economy and Agri-technology (iBO), Center for Research and Technology—Hellas (CERTH), Thessaloniki GR57001, Greece
    Department of Energy Systems, Faculty of Technology, University of Thessaly, Geopolis Campus Ring Road of Larissa-Trikala, Larissa GR41500, Greece)

  • Dionysis Bochtis

    (Institute for Bio-economy and Agri-technology (iBO), Center for Research and Technology—Hellas (CERTH), Thessaloniki GR57001, Greece)

Abstract

The present research study explores three types of neural network approaches for forecasting natural gas consumption in fifteen cities throughout Greece; a simple perceptron artificial neural network (ANN), a state-of-the-art Long Short-Term Memory (LSTM), and the proposed deep neural network (DNN). In this research paper, a DNN implementation is proposed where variables related to social aspects are introduced as inputs. These qualitative factors along with a deeper, more complex architecture are utilized for improving the forecasting ability of the proposed approach. A comparative analysis is conducted between the proposed DNN, the simple ANN, and the advantageous LSTM, with the results offering a deeper understanding the characteristics of Greek cities and the habitual patterns of their residents. The proposed implementation shows efficacy on forecasting daily values of energy consumption for up to four years. For the evaluation of the proposed approach, a real-life dataset for natural gas prediction was used. A detailed discussion is provided on the performance of the implemented approaches, the ANN and the LSTM, that are characterized as particularly accurate and effective in the literature, and the proposed DNN with the inclusion of the qualitative variables that govern human behavior, which outperforms them.

Suggested Citation

  • Athanasios Anagnostis & Elpiniki Papageorgiou & Dionysis Bochtis, 2020. "Application of Artificial Neural Networks for Natural Gas Consumption Forecasting," Sustainability, MDPI, vol. 12(16), pages 1-29, August.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:16:p:6409-:d:396667
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    References listed on IDEAS

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    Cited by:

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    3. Darko B. Vukovic & Lubov Spitsina & Ekaterina Gribanova & Vladislav Spitsin & Ivan Lyzin, 2023. "Predicting the Performance of Retail Market Firms: Regression and Machine Learning Methods," Mathematics, MDPI, vol. 11(8), pages 1-23, April.
    4. Irene M. Zarco-Soto & Fco. Javier Zarco-Soto & Pedro J. Zarco-Periñán, 2021. "Influence of Population Income on Energy Consumption and CO 2 Emissions in Buildings of Cities," Sustainability, MDPI, vol. 13(18), pages 1-18, September.
    5. Jun Yuan & Jiang Zhu & Victor Nian, 2020. "Neural Network Modeling Based on the Bayesian Method for Evaluating Shipping Mitigation Measures," Sustainability, MDPI, vol. 12(24), pages 1-14, December.
    6. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.

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