Smart Energy Management in Hotels: Leveraging IoT and Smart Meter Data

The hotel sector plays a crucial role in the global economy, providing significant social and economic benefits, such as job creation and contributions to local development. However, it is also the second most energy-intensive segment of the tourism industry, with energy costs representing 3% to 6% of hotel operating costs and approximately 60% of its CO2 emissions. In response to rising energy costs, stricter environmental regulations, and increasing customer awareness of sustainability, there is growing pressure on hoteliers to adopt energy-saving measures and enhance their environmental performance and sustainability. While a number of researchers have studied energy usage intensity in the hotel sector, there is a lack of research on the breakdown of real-time energy data relating to the hotel sector. Furthermore, there is a lack of research on how technology, especially the Internet of Things (IoT), can be used to reduce energy wastage and improve the overall energy efficiency of hotel operations, thus improving the hotels' environmental performance. Furthermore, the current literature on IoT for energy optimisation primarily focuses on residential homes and offices, leaving a gap in the hotel sector. This research studies the integration of IoT and Smart energy metering for better energy efficiency in the hotel sector. The methodology applied in this research followed the Six Sigma DMAIC (Define, Measure, Analyse, Improve Control) framework using real-time energy consumption data captured over the last ten years for 11 hotels (4*, 4* Business, and 5*). Halfhourly energy usage data from two hotels (one 4* and one 5*) were monitored and analysed using the Pearson correlation coefficient and the linear regression to precisely evaluate the impact of the degree-day and occupancy on energy consumption. The study also delved into the factors affecting energy consumption, static factors such as the building floor area, building characteristics, and size and condition of energy use systems and dynamic factors such as weather and occupancy. Each hotel's Energy Use Intensity (EUI) was assessed and compared against CIBSE Benchmarks. Five potential IoT and smart submetering applications were deployed and experimented with in one hotel. At the last stage, the author proposed a long-term energy control model that utilises IoT sensors integrated with Building Management Systems (BMS) and Lighting control units. The study's key finding was that the Mean Energy Use Intensity (MEUI) was lower for the 4* Business category, at 338 kWh/ m², compared to the standard 4* category, at 445 kWh/m², and 5-star hotels, at 462 kWh/ m², these exceed the CIBSE good practice benchmark by 24%, 55%, and 37% for the respective categories. For a standard 5* hotel. The Pearson correlation coefficient (PCC) for electricity consumption and occupancy was 0.19. At the same time, a 5- star hotel provided with IoT showed a higher PCC at 0.37 for occupancy. The results of the IoT application experiments in the hotel were: 1- Obtained a dynamic and instant breakdown of energy usage. 2- Temperature monitoring systems for fridges and freezers saved energy and prevented food waste. 3- Bedroom curtains control: managed to minimise the amount of cold air supplied to a room with closed curtains compared to the same size room with curtains opened during hot weather. 4- The food warmer power control system saved between 31226 kWh to 52980 kWh per year, equating to £14,285 and 7,026 to 11.920 kg CO2 ii annually. 5- The fault detection system discovered a few faults through energy consumption anomalies saved on one instance of anomalies 228 MWh per year, equating to £66,047 and 51,298 kg CO2 annually. Implementing various IoT applications in the studied hotel achieved energy savings of 68 % in the lighting of guest rooms and 34.4% in lighting public areas and saving 135,075 kg and 55,986 kg of CO2 emissions, respectively. After completing a comprehensive energy analysis, the author presented a tailored framework for hotel energy management designed to meet any hotel's specific needs. The framework comprises five critical components aligned with the hotel's energy consumption patterns. Strategies focus on reducing energy waste by optimising lighting and HVAC systems based on the occupancy of each area for both guests and staff. This customised approach has the potential to achieve up to 20% energy savings in certain areas without compromising service quality. Comparing energy consumption for one year with the same period the previous year showed reductions of 7.6% on the main hotel electricity supply lines. 319.5 MWh is equal to 71.9 tons of CO2 emissions. Further research is needed to explore the use of machine learning and artificial intelligence (AI) to analyse the collected data and widen the horizons for energy management.