Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Using Different ML Algorithms and Hyperparameter Optimisation to Predict Heat Meters' Failures

Version 1 : Received: 25 July 2019 / Approved: 28 July 2019 / Online: 28 July 2019 (16:26:47 CEST)

A peer-reviewed article of this Preprint also exists.

Pałasz, P.; Przysowa, R. Using Different ML Algorithms and Hyperparameter Optimization to Predict Heat Meters’ Failures. Appl. Sci. 2019, 9, 3719. Pałasz, P.; Przysowa, R. Using Different ML Algorithms and Hyperparameter Optimization to Predict Heat Meters’ Failures. Appl. Sci. 2019, 9, 3719.

Journal reference: Appl. Sci. 2019, 9, 3719
DOI: 10.3390/app9183719


The need to increase the energy efficiency of buildings as well as the use of local renewable heat sources has caused that heat meters are used not only to calculate the consumed energy but also for the active management of central heating systems. Increasing the reading frequency and the use of measurement data to control the heating system expands the requirements for the reliability of heat meters. The aim of the research is to analyse a large set of meters in the real network and predict their faults to avoid inaccurate readings, incorrect billing, heating system disruption and unnecessary maintenance. The reliability analysis of heat metres, based on historical data collected over several years, shows some regularities which cannot be easily described by physics-based models. The failure rate is almost constant and does depend on the past but is a non-linear combination of state variables. To predict meters' failures in the next settlement period, three independent machine learning models are implemented and compared with selected metrics because even the high performance of a single model (87\% True Positive for Neural Network) may be insufficient to make a maintenance decision. Additionally, performing hyperparameters optimisation boosts models' performance by a few percent. Finally, three improved models are used to build an ensemble classifier which outperforms the individual models. The proposed procedure ensures the high efficiency of fault detection (>95\%), while maintaining overfitting at the minimum level. The methodology is universal and can be utilised to study the reliability and predict faults of other types of meters and different objects with the constant failure rate.

Subject Areas

heat meter; district heating; fault detection; predictive maintenance; Machine Learning (ML); Artificial Neural Network (ANN); Bagging Decision Tree (BDT); Support Vector Machines (SVM); hyperparameter optimisation; ensemble model

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