Evaluating Energy Savings in Smart Hotel Rooms: An IoT-Based Framework for Intelligent HVAC Control

Heating, ventilation and air-conditioning (HVAC) systems are a major source of hotel energy use, particularly in tropical climates, but evidence on Internet of Things (IoT)-based control under real hotel operation remains limited. This study field-evaluated a commercial, interoperable IoT platform for reducing HVAC energy use in a guest room of a four-star resort in Cozumel, Mexico. Four intelligent control scenarios were implemented with IoT devices: an infrared controller, door/window and motion sensors, a presence sensor, a temperature-humidity sensor, and a Zigbee gateway bridged to the hotel Wi-Fi network. Indoor and outdoor conditions were recorded hourly over 44 valid 24 h periods, divided equally between a baseline period and an intelligent-control period, and HVAC energy consumption was estimated, without electrical metering, with a rule-based thermal-response model based on indoor temperature, relative humidity, and the outdoor–indoor temperature difference. With outdoor temperature higher by 1.6 °C and outdoor relative humidity higher by 3.8 percentage points during the intervention, estimated HVAC energy consumption decreased by 30.3% (95% CI: 10.9–45.6%), from 943.6 kW h to 657.3 kW h; this is an unadjusted difference between two sequential periods. When the intervention occupied- and unoccupied-hour energy rates were standardised to the baseline occupancy mix, the corresponding reduction was 26.3%. The confidence interval applies to the unadjusted one-room period contrast and is not adjusted for occupancy or outdoor weather conditions. Routine over-cooling was reduced and no guest complaints were recorded. The observed period difference was associated mainly with changes in the timing and estimated load of HVAC operation rather than with a uniform reduction in cooling demand: operating hours fell by only 6.8%, and the reduction in mean hourly estimated HVAC energy was greater during unoccupied than occupied periods. Differences in occupancy composition also contributed to the whole-period contrast. Scenario activation frequency was not proportional to estimated energy demand and so cannot serve as a proxy for scenario effectiveness; isolating the contribution of individual scenarios would require scenario-specific measurement. The study completed the first three of the five framework stages (scenario definition, IoT data acquisition and performance evaluation) in one guest room. The results are specific to this room, period and operating conditions, should not be generalised to other hotels or climates, and require validation against metered HVAC electricity.