Data Driven HVAC Energy Management in the Commercial Real Estate Industry: Integrating Predictive IoT Control with Facility Management Strategies

Abstract

The energy use of heating, ventilation and air conditioning (HVAC) equipment is a significant part of energy consumption in high-rise commercial buildings, particularly in tropical countries where there are high temperatures and humidity. In cities like Jakarta, strong demand for space cooling results in HVAC systems running heavily, leading to a rise in energy use and building operation cost. This is a problem for building administrators who want to increase energy efficiency without compromising thermal comfort for building occupants. This research is focused on exploring the potential of a predictive HVAC control system based on Internet of Things (IoT) technology to enhance the energy efficiency of building operation in commercial buildings. This work adopts a quantitative engineering approach with operational management analysis. The proposed system was an IoT-based real-time monitoring and data acquisition system to acquire building environment data, a data-driven thermal load prediction model, and an adaptive control using a proportional–integral–derivative (PID) controller. This setup was evaluated in a 34-storey office tower in the heart of Jakarta’s business district. The results indicate that a predictive IoT based HVAC control system can reduce HVAC energy consumption on average by ±18% over the traditional approaches while ensuring stable indoor thermal comfort. This study is significant in that it demonstrates how to integrate IoT technology to commercial buildings operation to enable smart operation and energy efficiency in a tropical urban setting.