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Machine Learning Short-Term Energy Consumption Forecasting for Microgrids in a Manufacturing Plant

Author

Listed:
  • Maciej Slowik

    (Faculty of Engineering Management, Bialystok University of Technology, Wiejska 45A, 15-351 Bialystok, Poland)

  • Wieslaw Urban

    (Faculty of Engineering Management, Bialystok University of Technology, Wiejska 45A, 15-351 Bialystok, Poland)

Abstract

Energy production and supply are important challenges for civilisation. Renewable energy sources present an increased share of the energy supply. Under these circumstances, small-scale grids operating in small areas as fully functioning energy systems are becoming an interesting solution. One crucial element to the success of micro-grid structures is the accurate forecasting of energy consumption by large customers, such as factories. This study aimed to develop a universal forecasting tool for energy consumption by end-use consumers. The tool estimates energy use based on real energy-consumption data obtained from a factory or a production machine. This model allows the end-users to be equipped with an energy demand prediction, enabling them to participate more effectively in the smart grid energy market. A single, long short-term memory (LSTM)-layer-based artificial neural network model for short-term energy demand prediction was developed. The model was based on a manufacturing plant’s energy consumption data. The model is characterised by high prediction capability, and it predicted energy consumption, with a mean absolute error value of 0.0464. The developed model was compared with two other methodologies.

Suggested Citation

  • Maciej Slowik & Wieslaw Urban, 2022. "Machine Learning Short-Term Energy Consumption Forecasting for Microgrids in a Manufacturing Plant," Energies, MDPI, vol. 15(9), pages 1-16, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3382-:d:809621
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    Cited by:

    1. Wieslaw Urban, 2022. "Energy Savings in Production Processes as a Key Component of the Global Energy Problem—The Introduction to the Special Issue of Energies," Energies, MDPI, vol. 15(14), pages 1-4, July.
    2. Dimitrios K. Panagiotou & Anastasios I. Dounis, 2022. "Comparison of Hospital Building’s Energy Consumption Prediction Using Artificial Neural Networks, ANFIS, and LSTM Network," Energies, MDPI, vol. 15(17), pages 1-25, September.
    3. Bartłomiej Mroczek & Paweł Pijarski, 2022. "Machine Learning in Operating of Low Voltage Future Grid," Energies, MDPI, vol. 15(15), pages 1-30, July.

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