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Development of a SVM Prediction Model to Optimize the Energy Consumption of Industrial Installations by Detecting and Classifying Errors at an Early S

M. Stul 1, K. Stul 1, R. Leenders 2, and L. Butaye 2
1. Department of Electrical Engineering, KU Leuven, Belgium
2. Departement of Energytechnology, Odisee, Belgium
Abstract—Considerable energy savings in industrial environment are possible in an industrial environment by detecting installations not working at their optimum operating point. The present paper proposes a new generalized data driven FDD method capable of automatically detecting the abnormal energy demand of different types of installations or machines based on process data. The paper contains a comprehensive overview of the research, focusing on a trade-off between performance and computing time together with minimizing the human input. The proposed method contains an automated feature selection, a hyper-parameter optimization of the chosen SVM regression algorithm and a residual control algorithm. The method was tested in several industrial installations and two case studies are presented to demonstrate the performance of the proposed method, while underlining the significance of a decent number of relevant features. 
 
Index Terms—energy prediction, SVM-regression, FFD, energy management

Cite: M. Stul, K. Stul, R. Leenders, and L. Butaye, "Development of a SVM Prediction Model to Optimize the Energy Consumption of Industrial Installations by Detecting and Classifying Errors at an Early S," International Journal of Mechanical Engineering and Robotics Research, Vol. 6, No. 2, pp. 108-113, March 2017. DOI: 10.18178/ijmerr.6.2.108-113
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