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Using Industry 4.0’s Big Data and IoT to Perform Feature-Based and Past Data-Based Energy Consumption Predictions

Author

Listed:
  • Jonathan Gumz

    (Department of Production Engineering, Universidade Federal de Santa Catarina, Florianópolis 88040-900, Brazil)

  • Diego Castro Fettermann

    (Department of Production Engineering, Universidade Federal de Santa Catarina, Florianópolis 88040-900, Brazil)

  • Enzo Morosini Frazzon

    (Department of Production Engineering, Universidade Federal de Santa Catarina, Florianópolis 88040-900, Brazil)

  • Mirko Kück

    (Department of Production Engineering, Universität Bremen, 28359 Bremen, Germany)

Abstract

Industry 4.0 and its technologies allow advancements in communications, production and management efficiency across several segments. In smart grids, essential parts of smart cities, smart meters act as IoT devices that can gather data and help the management of the sustainable energy matrix, a challenge that is faced worldwide. This work aims to use smart meter data and household features data to seek the most appropriate methods of energy consumption prediction. Using the Cross-Industry Standard Process for Data Mining (CRISP-DM) method, Python Platform, and several prediction methods, prediction experiments were performed with household feature data and past consumption data of over 470 smart meters that gathered data for three years. Support vector machines, random forest regression, and neural networks were the best prediction methods among the ones tested in the sample. The results help utilities (companies that maintain the infrastructure for public services) to offer better contracts to new households and to manage their smart grid infrastructure based on the forecasted demand.

Suggested Citation

  • Jonathan Gumz & Diego Castro Fettermann & Enzo Morosini Frazzon & Mirko Kück, 2022. "Using Industry 4.0’s Big Data and IoT to Perform Feature-Based and Past Data-Based Energy Consumption Predictions," Sustainability, MDPI, vol. 14(20), pages 1-34, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13642-:d:949314
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    1. Faruqui, Ahmad & Harris, Dan & Hledik, Ryan, 2010. "Unlocking the [euro]53 billion savings from smart meters in the EU: How increasing the adoption of dynamic tariffs could make or break the EU's smart grid investment," Energy Policy, Elsevier, vol. 38(10), pages 6222-6231, October.
    2. Li, Ling, 2018. "China's manufacturing locus in 2025: With a comparison of “Made-in-China 2025” and “Industry 4.0”," Technological Forecasting and Social Change, Elsevier, vol. 135(C), pages 66-74.
    3. Hilary S. Boudet, 2019. "Public perceptions of and responses to new energy technologies," Nature Energy, Nature, vol. 4(6), pages 446-455, June.
    4. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    5. Buchanan, Kathryn & Banks, Nick & Preston, Ian & Russo, Riccardo, 2016. "The British public’s perception of the UK smart metering initiative: Threats and opportunities," Energy Policy, Elsevier, vol. 91(C), pages 87-97.
    6. Zhang, Tao & Siebers, Peer-Olaf & Aickelin, Uwe, 2016. "Simulating user learning in authoritative technology adoption: An agent based model for council-led smart meter deployment planning in the UK," Technological Forecasting and Social Change, Elsevier, vol. 106(C), pages 74-84.
    7. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
    8. Nikhlesh Pathik & Rajeev Kumar Gupta & Yatendra Sahu & Ashutosh Sharma & Mehedi Masud & Mohammed Baz, 2022. "AI Enabled Accident Detection and Alert System Using IoT and Deep Learning for Smart Cities," Sustainability, MDPI, vol. 14(13), pages 1-24, June.
    9. Hug March & Álvaro-Francisco Morote & Antonio-Manuel Rico & David Saurí, 2017. "Household Smart Water Metering in Spain: Insights from the Experience of Remote Meter Reading in Alicante," Sustainability, MDPI, vol. 9(4), pages 1-18, April.
    10. Vinoth Kumar Ponnusamy & Padmanathan Kasinathan & Rajvikram Madurai Elavarasan & Vinoth Ramanathan & Ranjith Kumar Anandan & Umashankar Subramaniam & Aritra Ghosh & Eklas Hossain, 2021. "A Comprehensive Review on Sustainable Aspects of Big Data Analytics for the Smart Grid," Sustainability, MDPI, vol. 13(23), pages 1-35, December.
    11. Kück, Mirko & Freitag, Michael, 2021. "Forecasting of customer demands for production planning by local k-nearest neighbor models," International Journal of Production Economics, Elsevier, vol. 231(C).
    12. George Lăzăroiu & Luminița Ionescu & Mihai Andronie & Irina Dijmărescu, 2020. "Sustainability Management and Performance in the Urban Corporate Economy: A Systematic Literature Review," Sustainability, MDPI, vol. 12(18), pages 1-13, September.
    13. Shan Zhou & Douglas S. Noonan, 2019. "Justice Implications of Clean Energy Policies and Programs in the United States: A Theoretical and Empirical Exploration," Sustainability, MDPI, vol. 11(3), pages 1-20, February.
    14. Arora, Siddharth & Taylor, James W., 2016. "Forecasting electricity smart meter data using conditional kernel density estimation," Omega, Elsevier, vol. 59(PA), pages 47-59.
    15. Fekri, Mohammad Navid & Patel, Harsh & Grolinger, Katarina & Sharma, Vinay, 2021. "Deep learning for load forecasting with smart meter data: Online Adaptive Recurrent Neural Network," Applied Energy, Elsevier, vol. 282(PA).
    16. Jung-Hoon Kim & Joo-Young Kim, 2022. "How Should the Structure of Smart Cities Change to Predict and Overcome a Pandemic?," Sustainability, MDPI, vol. 14(5), pages 1-17, March.
    17. Souhaib Ben Taieb & James W. Taylor & Rob J. Hyndman, 2021. "Hierarchical Probabilistic Forecasting of Electricity Demand With Smart Meter Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(533), pages 27-43, March.
    18. Buryk, Stephen & Mead, Doug & Mourato, Susana & Torriti, Jacopo, 2015. "Investigating preferences for dynamic electricity tariffs: The effect of environmental and system benefit disclosure," Energy Policy, Elsevier, vol. 80(C), pages 190-195.
    19. Campillo, Javier & Dahlquist, Erik & Wallin, Fredrik & Vassileva, Iana, 2016. "Is real-time electricity pricing suitable for residential users without demand-side management?," Energy, Elsevier, vol. 109(C), pages 310-325.
    20. Tatiana Tucunduva Philippi Cortese & Jairo Filho Sousa de Almeida & Giseli Quirino Batista & José Eduardo Storopoli & Aaron Liu & Tan Yigitcanlar, 2022. "Understanding Sustainable Energy in the Context of Smart Cities: A PRISMA Review," Energies, MDPI, vol. 15(7), pages 1-38, March.
    21. Babai, Zied & Boylan, John E. & Kolassa, Stephan & Nikolopoulos, Konstantinos, 2016. "Supply chain forecasting: Theory, practice, their gap and the futureAuthor-Name: Syntetos, Aris A," European Journal of Operational Research, Elsevier, vol. 252(1), pages 1-26.
    22. Ellabban, Omar & Abu-Rub, Haitham, 2016. "Smart grid customers' acceptance and engagement: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 1285-1298.
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    1. Gumz, Jonathan & Stephenson, Janet R. & Fettermann, Diego C. & Wooliscroft, Ben, 2024. "A mixed-method analysis of New Zealand's smart meter rollout experience," Utilities Policy, Elsevier, vol. 90(C).

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