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Enhancement of a Short-Term Forecasting Method Based on Clustering and kNN: Application to an Industrial Facility Powered by a Cogenerator

Author

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  • Giulio Vialetto

    (Department of Management and Engineering, University of Padova, 36100 Vicenza, Italy)

  • Marco Noro

    (Department of Management and Engineering, University of Padova, 36100 Vicenza, Italy)

Abstract

In recent years, collecting data is becoming easier and cheaper thanks to many improvements in information technology (IT). The connection of sensors to the internet is becoming cheaper and easier (for example, the internet of things, IOT), the cost of data storage and data processing is decreasing, meanwhile artificial intelligence and machine learning methods are under development and/or being introduced to create values using data. In this paper, a clustering approach for the short-term forecasting of energy demand in industrial facilities is presented. A model based on clustering and k-nearest neighbors (kNN) is proposed to analyze and forecast data, and the novelties on model parameters definition to improve its accuracy are presented. The model is then applied to an industrial facility (wood industry) with contemporaneous demand of electricity and heat. An analysis of the parameters and the results of the model is performed, showing a forecast of electricity demand with an error of 3%.

Suggested Citation

  • Giulio Vialetto & Marco Noro, 2019. "Enhancement of a Short-Term Forecasting Method Based on Clustering and kNN: Application to an Industrial Facility Powered by a Cogenerator," Energies, MDPI, vol. 12(23), pages 1-16, November.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:23:p:4407-:d:288882
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    References listed on IDEAS

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    Cited by:

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    5. Kei Hirose & Keigo Wada & Maiya Hori & Rin-ichiro Taniguchi, 2020. "Event Effects Estimation on Electricity Demand Forecasting," Energies, MDPI, vol. 13(21), pages 1-20, November.

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