This Python script developed an approach that uses various Explainable AI techniques to interpret the fault detection and diagnosis model results for Air Handling Units.
Heating, ventilation, and air conditioning (HVAC) systems consume a significant amount of energy in buildings and can experience faults when not properly maintained, resulting in increased energy consumption and maintenance costs. Developing an efficient Fault Detection and Diagnosis (FDD) system is essential to address this issue. Due to data availability, researchers are leaning towards data-driven approaches that use Machine Learning (ML) algorithms. However, the lack of transparency of ML algorithms makes stakeholders hesitant to adopt them for FDD. Therefore, this study examines using eXplainable Artificial Intelligence (XAI) techniques to improve FDD in Air Handling Units, enhancing their interpretability. Four ML techniques were evaluated based on fault detection rate and F1 score, and XGBoost was determined to be the optimal choice for the FDD model. The study used SHAP (Shapley Additive explanations) to interpret the developed model. The SHAP summary plot and the SHAP waterfall plot are used for global and local explanations, respectively. The study successfully demonstrated the effectiveness of XAI techniques in improving the transparency and interpretability of ML models for FDD in Air Handling Units.