Data-Driven Predictive Maintenance: HVAC Health Prognostics Using Power Consumption and Weather Data
Abstract
Data-driven predictive maintenance for heat, ventilation, and air conditioning (HVAC) systems has gained much popularity over recent years due to the increasing availability of integrated internet of things (IoT) sensors capable of reporting HVAC internal operational data. Most existing predictive maintenance methods are designed to analyse these internal operational data for maintenance decision making. However, these methods are not applicable to HVAC systems that are not equipped with internal IoT sensors. Consequently, we propose an AutoEncoder and Artificial Neural Network based HVAC Health Prognostics framework (AE-ANN-HP) that classifies the health condition of HVAC systems using only daily power consumption and outside temperature readings, both of which are easy to obtain for non-IoT enabled HVAC systems. AE-ANN-HP when evaluated with three types of autoencoders all show an increase in performance when compared to existing HVAC health prognostics methods in terms of classification accuracy.