In the recent years, collecting data is becoming easier and cheaper thanks to many improvements on information technology (IT). Connection of the sensors to the net is becoming cheaper and easier (for example IoT—internet of thing), cost of data storage and data processing is decreasing, meanwhile artificial intelligence and machine learning methods are under developments and/or introduction to create values on data. In this paper it is presented a clustering approach for short-term forecasting of energy demand in industrial facilities. A model based on clustering and 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 on the parameters and the results of the model are performed, showing a forecast of electricity demand with an error of 3%.

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

Giulio Vialetto
;
Marco Noro
2019

Abstract

In the recent years, collecting data is becoming easier and cheaper thanks to many improvements on information technology (IT). Connection of the sensors to the net is becoming cheaper and easier (for example IoT—internet of thing), cost of data storage and data processing is decreasing, meanwhile artificial intelligence and machine learning methods are under developments and/or introduction to create values on data. In this paper it is presented a clustering approach for short-term forecasting of energy demand in industrial facilities. A model based on clustering and 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 on the parameters and the results of the model are performed, showing a forecast of electricity demand with an error of 3%.
2019
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3315846
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