Boiler Efficiency and Performance Optimization in District Heating and Cooling Systems With Machine Learning Models

Abstract

This study focuses on the detection and analysis of boiler efficiency degradation in District Heating and Cooling (DHC) substations. The research presents an innovative approach to optimize boiler efficiency under different scenarios. Although DHC systems provide both heating and cooling services, this study focuses specifically on heating substations. In this context, various machine learning algorithms have been applied to effectively detect boiler efficiency degradation, and hyper-parameter adjustments have been performed using Bayesian optimization to improve the performance of the models. As a result of the analyses, the Gradient Boosting Regressor model showed significantly higher performance compared to other machine learning algorithms. The model successfully predicted the decline in boiler efficiency with an accuracy of 97.8%, and the Matthews Correlation Coefficient (MCC) value was recorded as 0.952. These results show that Gradient Boosting Regressor based approaches provide an effective solution for fault detection and diagnosis in district heating systems. In conclusion, this study provides both theoretical and practical contributions to the optimization of boiler efficiency, fault detection and diagnosis in DHC systems. The solutions offered by the study have the potential to increase the reliability and efficiency of the systems.