Travel reservation is an important way to improve tourist experiences and digitally manage tourist attractions in the COVID-19 context. However, few studies have focused on the online reservation intentions of tourist attractions and its influencing factors. Based on the theory of the technology acceptance model (TAM), two variables (perceived risk and government policy) are introduced to expand on the theoretical model. This study investigates the influence of subjective norms, government policy, perceived usefulness, perceived ease of use, and perceived risk on reservation intentions of tourist attractions. An online survey was conducted in China, and 255 questionnaires were collected. The data were analysed using SPSS 26.0 and AMOS 28.0 to construct a structural equation modelling and analyse the path. The findings show that (1) subjective norms have no significant impact on reservation behaviours under voluntary situations; (2) perceived usefulness positively affects tourists’ reservation intention; and (3) perceived risk has a significant negative impact on reservation intention, and government policy is the main factor affecting tourists’ reservation intentions. These findings enhance the understanding of tourists’ reservation intentions and extend the TAM theory. From the practice perspective, tourist attraction operators should continue to strengthen the construction of the reservation system, improve tourists’ experiences, reduce the perceived risk of tourists, and other stakeholders such as the government should strengthen cooperation, promote the reservation system, and create a good reservation atmosphere.
At this stage, due to the increasing use of electric vehicles, the position of electric vehicle load scheduling in grid power scheduling is becoming more and more important. Effective electric vehicle power dispatching can balance the peak-valley difference of power dispatching, increase the power supply utilization rate of power grid dispatching, and reduce the power supply pressure of line transformer. The load forecast can describe the user’s electricity consumption habits in the next period of time, and can provide important data basis for power dispatching. This paper summarizes the research status of electric vehicle charging load, analyzes traditional charging load research methods, propose a charging load forecasting method combining XGBoost(Extreme Gradient Boosting) and LSTM (Long Short Term Memory Network), And use the data of a charging station in Jiangsu to verify the calculation example. The proposed method is based on the prediction results of the XGBoost model for feature engineering, extracting data features using phase space reconstruction techniques and statistical methods. In addition, training the LSTM model for load prediction. Based on the charging record data of domestic charging stations, this paper applies the artificial intelligence method to the charging load forecast of domestic charging stations for the first time. The charging station load forecasting method studied in this paper can support the regional load forecasting research of electric vehicles with high permeability, and further optimize power dispatching.
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