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A Systematic Review on Predicting and Forecasting the Electrical Energy Consumption in the Manufacturing Industry

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  • Jessica Walther

    (Institute of Production Management, Technology and Machine Tools (PTW), Department Mechanical Engineering, Technical University of Darmstadt, Otto-Berndt-Str. 2, 64287 Darmstadt, Germany)

  • Matthias Weigold

    (Institute of Production Management, Technology and Machine Tools (PTW), Department Mechanical Engineering, Technical University of Darmstadt, Otto-Berndt-Str. 2, 64287 Darmstadt, Germany)

Abstract

In the context of the European Green Deal, the manufacturing industry faces environmental challenges due to its high demand for electrical energy. Thus, measures for improving the energy efficiency or flexibility are applied to address this problem in the manufacturing industry. In order to quantify energy efficiency or flexibility potentials, it is often necessary to predict or forecast the energy consumption. This paper presents a systematic review of state-of-the-art of existing approaches to predict or forecast the energy consumption in the manufacturing industry. Seventy-two articles are classified according to the defined categories System Boundary, Modelling Technique, Modelling Focus, Modelling Horizon, Modelling Perspective, Modelling Purpose and Model Output. Based on the reviewed articles future research activities are derived.

Suggested Citation

  • Jessica Walther & Matthias Weigold, 2021. "A Systematic Review on Predicting and Forecasting the Electrical Energy Consumption in the Manufacturing Industry," Energies, MDPI, vol. 14(4), pages 1-24, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:968-:d:498275
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    References listed on IDEAS

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    Cited by:

    1. Alexandra L’Heureux & Katarina Grolinger & Miriam A. M. Capretz, 2022. "Transformer-Based Model for Electrical Load Forecasting," Energies, MDPI, vol. 15(14), pages 1-23, July.
    2. Dirk Deschrijver, 2021. "Special Issue: “Improving Energy Efficiency through Data-Driven Modeling, Simulation and Optimization”," Energies, MDPI, vol. 14(6), pages 1-3, March.
    3. Joanna Henzel & Łukasz Wróbel & Marcin Fice & Marek Sikora, 2022. "Energy Consumption Forecasting for the Digital-Twin Model of the Building," Energies, MDPI, vol. 15(12), pages 1-21, June.
    4. Demirci, Alpaslan & Öztürk, Zafer & Tercan, Said Mirza, 2023. "Decision-making between hybrid renewable energy configurations and grid extension in rural areas for different climate zones," Energy, Elsevier, vol. 262(PA).
    5. Diogo M. F. Izidio & Paulo S. G. de Mattos Neto & Luciano Barbosa & João F. L. de Oliveira & Manoel Henrique da Nóbrega Marinho & Guilherme Ferretti Rissi, 2021. "Evolutionary Hybrid System for Energy Consumption Forecasting for Smart Meters," Energies, MDPI, vol. 14(7), pages 1-19, March.
    6. Karol Bot & Samira Santos & Inoussa Laouali & Antonio Ruano & Maria da Graça Ruano, 2021. "Design of Ensemble Forecasting Models for Home Energy Management Systems," Energies, MDPI, vol. 14(22), pages 1-37, November.
    7. Marian Kampik & Marcin Fice & Adam Pilśniak & Krzysztof Bodzek & Anna Piaskowy, 2023. "An Analysis of Energy Consumption in Small- and Medium-Sized Buildings," Energies, MDPI, vol. 16(3), pages 1-21, February.

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