- Subsurface Temperature Estimation from Sea Surface Data Using Neural Network Models in the Western Pacific Ocean
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- 关键字：impact factors; energy use intensity; STIRPAT model; energy saving
- 摘要：Energy consumption of hotel can be a hot topic in smart city. To find the key impact factors of energy consumption to realize the energy saving target. This paper examined the impact factors of occupancy rate, unit area of revenue, temperature factor and unit revenue of energy consumption. However, the current study on energy consumption of hotel is limited to the relationship with the single impact factor at home and abroad. In this paper, by taking the energy consumption data of a five-star hotel as a case, we explored the impact factors of occupancy rate, unit area of revenue, temperature factor and unit revenue of energy consumption on energy use intensity (EUI) change based on the extended STIRPAT model. Empirical results indicate that factors such as occupancy rate, unit area of revenue, and unit revenue of energy consumption can cause an increase in energy consumption of the hotel, but temperature factor can lead to a decrease in energy consumption of the hotel. The empirical results also reveal that the extended STIRPAT model can be used to predict and evaluate energy consumption of hotel. Finally, the study can provide managerial reference according to the regression results for energy saving management of hotel.
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