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Generic Machine Learning Approach for Very Short Term Load Forecasting of Production Machines

Walther, Jessica and Dietrich, Bastian and Abele, Eberhard (2019):
Generic Machine Learning Approach for Very Short Term Load Forecasting of Production Machines.
In: Proceedings of 11th International Conference on Applied Energy, August 12-15, Västerås (Sweden), pp. Paper ID -589, [Conference or Workshop Item]

Abstract

With the ongoing digitalization of industrial production, an increasing number of energy measuring points are installed in manufacturing environments which enable promising use cases for machine learning applications. This paper presents a generic machine learning approach to forecast the very short term load of production machines which can be utilized as decision support basis for control schemes and measures to decrease energy costs. The presented approach is developed and evaluated on production machines of the ETA research factory at the Technische Universität Darmstadt. The results indicate that the developed approach is feasible and creates a precise very short term load forecasting model for different production machines.

Item Type: Conference or Workshop Item
Erschienen: 2019
Creators: Walther, Jessica and Dietrich, Bastian and Abele, Eberhard
Title: Generic Machine Learning Approach for Very Short Term Load Forecasting of Production Machines
Language: English
Abstract:

With the ongoing digitalization of industrial production, an increasing number of energy measuring points are installed in manufacturing environments which enable promising use cases for machine learning applications. This paper presents a generic machine learning approach to forecast the very short term load of production machines which can be utilized as decision support basis for control schemes and measures to decrease energy costs. The presented approach is developed and evaluated on production machines of the ETA research factory at the Technische Universität Darmstadt. The results indicate that the developed approach is feasible and creates a precise very short term load forecasting model for different production machines.

Title of Book: Proceedings of 11th International Conference on Applied Energy, August 12-15
Place of Publication: Västerås (Sweden)
Uncontrolled Keywords: Load forecasting, machine learning, feature selection, feature engineering, artificial neural networks
Divisions: 16 Department of Mechanical Engineering
16 Department of Mechanical Engineering > Institute of Production Technology and Machine Tools (PTW)
16 Department of Mechanical Engineering > Institute of Production Technology and Machine Tools (PTW) > ETA Energy Technologies and Applications in Production
Date Deposited: 24 Apr 2020 12:38
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