Construction of Hotel Energy Consumption Prediction and Management Scheme Based on Machine Learning

As demand for energy management in the hotel increases, energy efficiency and energy consumption costs are important research directions. This paper introduces the energy consumption prediction and management plan of the hotel based on machine learning, and adopts the hierarchical structure design. The power consumption prediction using the depth learning model such as LSTM, Gru, TCN, and so on is optimized by using the dynamic weight distribution strategy. The system integrates an adaptive alarm mechanism, provides real-time energy monitoring, and provides optimization suggestions. Experimental results show that this method has high predictive accuracy and stability under different loading conditions, effectively reducing the energy consumption of the hotel and improving the energy management efficiency. As a result, the energy management system based on machine learning has a high degree of adaptability under complicated circumstances and provides a wide range of applications.