Predictive Maintenance Strategies for Smart Buildings Using IoT and Time Series Analytics

Abstract

This study investigates the enhancement of predictive maintenance in smart buildings by the utilization of Internet of Things (IoT) sensors and Time Series Analytics, specifically applying Temporal Fusion Transformers (TFT).  The incorporation of IoT devices facilitates the ongoing surveillance of critical parameters including temperature, humidity, vibration, and energy usage throughout diverse building systems. Utilizing TFT's capacity to identify temporal relationships, the model accurately forecasts possible equipment failures, facilitating proactive maintenance measures. The evaluation revealed that the TFT model achieved a 15% enhancement in failure prediction accuracy compared to conventional time series forecasting models such as ARIMA and LSTM.  Moreover, by forecasting breakdowns 5–7 days ahead, maintenance teams might diminish emergency repair expenses by 25%, so prolonging the lifespan of essential building infrastructure by as much as 18%.  The findings indicate that the integration of IoT data with sophisticated machine learning methodologies substantially improves predictive maintenance, yielding cost reductions and operational efficiency in smart buildings.  The results highlight the revolutionary capability of TFT in enhancing maintenance procedures within smart environments, facilitating more sustainable building management.