Preprint Article Version 1 This version is not peer-reviewed

Predicting Heat Meters’ Failures With Selected Machine Learning Models

Version 1 : Received: 10 November 2018 / Approved: 13 November 2018 / Online: 13 November 2018 (04:41:07 CET)

How to cite: Pałasz, P.; Przysowa, R. Predicting Heat Meters’ Failures With Selected Machine Learning Models. Preprints 2018, 2018110293 (doi: 10.20944/preprints201811.0293.v1). Pałasz, P.; Przysowa, R. Predicting Heat Meters’ Failures With Selected Machine Learning Models. Preprints 2018, 2018110293 (doi: 10.20944/preprints201811.0293.v1).

Abstract

Heat metres are used to calculate the consumed energy in central heating systems. The subject of this article is to prepare a method of predicting a failure of a heat meter in the next settlement period. Predicting failures is essential to coordinate the process of exchanging the heat metres and to avoid inaccurate readings, incorrect billing and additional costs. The reliability analysis of heat metres was based on historical data collected over many years. Three independent models of machine learning were proposed, and they were applied to predict failures of metres. The efficiency of the models was confirmed and compared using the selected metrics. The optimisation of hyperparameters characteristics for each of models was successfully applied. The article shows that the diagnostics of devices does not have to rely only on newly collected information, but it is also possible to use the existing big data sets.

Subject Areas

machine Learning (ML); artificial neutral network (ANN); bagging decision tree (BDT); SUpport Vector Machines (SVM); no free lunch theorem (NFLT); hyperparameter optimisation; model comparison; heat meter

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