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Industrial Facility Electricity Consumption Forecast Using Artificial Neural Networks and Incremental Learning

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  • Daniel Ramos

    (GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Rua DR. Antonio Bernardino de Almeida, 431, 4200-072 Porto, Portugal
    Polytechnic of Porto, Rua DR. Antonio Bernardino de Almeida, 431, 4200-072 Porto, Portugal)

  • Pedro Faria

    (GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Rua DR. Antonio Bernardino de Almeida, 431, 4200-072 Porto, Portugal
    Polytechnic of Porto, Rua DR. Antonio Bernardino de Almeida, 431, 4200-072 Porto, Portugal)

  • Zita Vale

    (Polytechnic of Porto, Rua DR. Antonio Bernardino de Almeida, 431, 4200-072 Porto, Portugal)

  • João Mourinho

    (SISTRADE—Software Consulting, S.A., 4250-380 Porto, Portugal)

  • Regina Correia

    (SISTRADE—Software Consulting, S.A., 4250-380 Porto, Portugal)

Abstract

Society’s concerns with electricity consumption have motivated researchers to improve on the way that energy consumption management is done. The reduction of energy consumption and the optimization of energy management are, therefore, two major aspects to be considered. Additionally, load forecast provides relevant information with the support of historical data allowing an enhanced energy management, allowing energy costs reduction. In this paper, the proposed consumption forecast methodology uses an Artificial Neural Network (ANN) and incremental learning to increase the forecast accuracy. The ANN is retrained daily, providing an updated forecasting model. The case study uses 16 months of data, split in 5-min periods, from a real industrial facility. The advantages of using the proposed method are illustrated with the numerical results.

Suggested Citation

  • Daniel Ramos & Pedro Faria & Zita Vale & João Mourinho & Regina Correia, 2020. "Industrial Facility Electricity Consumption Forecast Using Artificial Neural Networks and Incremental Learning," Energies, MDPI, vol. 13(18), pages 1-18, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4774-:d:412844
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    References listed on IDEAS

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    Cited by:

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    3. Ragosebo Kgaugelo Modise & Khumbulani Mpofu & Olukorede Tijani Adenuga, 2021. "Energy and Carbon Emission Efficiency Prediction: Applications in Future Transport Manufacturing," Energies, MDPI, vol. 14(24), pages 1-15, December.
    4. Michael Wood & Emanuele Ogliari & Alfredo Nespoli & Travis Simpkins & Sonia Leva, 2023. "Day Ahead Electric Load Forecast: A Comprehensive LSTM-EMD Methodology and Several Diverse Case Studies," Forecasting, MDPI, vol. 5(1), pages 1-18, March.
    5. Aleksey I. Shinkevich & Irina G. Ershova & Farida F. Galimulina, 2022. "Forecasting the Efficiency of Innovative Industrial Systems Based on Neural Networks," Mathematics, MDPI, vol. 11(1), pages 1-25, December.
    6. Thomas Steens & Jan-Simon Telle & Benedikt Hanke & Karsten von Maydell & Carsten Agert & Gian-Luca Di Modica & Bernd Engel & Matthias Grottke, 2021. "A Forecast-Based Load Management Approach for Commercial Buildings Demonstrated on an Integration of BEV," Energies, MDPI, vol. 14(12), pages 1-25, June.
    7. Feras Alasali & Husam Foudeh & Esraa Mousa Ali & Khaled Nusair & William Holderbaum, 2021. "Forecasting and Modelling the Uncertainty of Low Voltage Network Demand and the Effect of Renewable Energy Sources," Energies, MDPI, vol. 14(8), pages 1-31, April.

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