Energy Saving Strategies for Tourism Smart Hotels Combining the Internet of Things and Machine Learning Algorithms

Under the background of the increasingly serious world energy crisis and the deteriorating ecological environment, tourism industries all over the world are facing severe challenges of energy conservation and emission reduction. As one of the main links, the research on energy saving of smart hotels is particularly important. Therefore, this project plans to integrate the Internet of Things and machine learning to build an effective hotel energy-saving management system. Firstly, real-time energy usage data of hotel rooms, such as temperature, humidity, light intensity, and guest activity, can be collected through Internet of Things (IoT) devices. Secondly, ML algorithms can be used to analyze this data, identify patterns of energy waste, and predict peaks and valleys in energy demand. Based on these predictions, the system can automatically adjust the operating status of air conditioning, lighting, and other equipment to achieve optimal energy utilization efficiency. In addition, this study can also explore the impact of user behavior on energy use and attempt to introduce user feedback mechanisms to enhance customer awareness and participation in energy conservation. From a time perspective, the energy consumption at night (18:00-24:00) is generally higher than during the day (00:00-06:00 and 06:00-12:00). The value of this study lies not only in helping hotel operators reduce energy costs and improve energy efficiency, but also in creating more comfortable and environmentally friendly accommodation experiences for guests, while contributing to the sustainable development of the global tourism industry. Overall, this study provides an innovative approach for hotel energy-saving management, demonstrating the potential and value of IoT and ML in practical applications.