Machine learning algorithms for classification of boiler faults using a simulated dataset

Abstract

Building performance has been shown to degrade significantly after commissioning, resulting in increased energy consumption and associated greenhouse gas emissions. Continuous Commissioning using existing sensor networks and IoT devices has the potential to minimize this waste by continually identifying system degradation and revising control strategies to adapt to real building performance. Due to its significant contribution to GHG emissions, building heating, particularly gas boiler systems are critical systems for detecting decreased performance. A review of boiler performance studies has been used to develop a set of common faults and degraded performance conditions, and these have been integrated into a MATLAB Simulink emulator to create a labelled dataset with approximately 27,500 cases for training and testing boiler fault classification models. Classification algorithms such as K-nearest neighbour, Decision tree, Random Forest and Naïve Bayes have been tested and the results show that decision tree methods gave the best prediction (97.8% accuracy) followed by Random forest (95.0%) and KNN for K = 3 (88.1%). Naïve Bayesian and KNN for K = 9 classification both gave poor results.