  The increase in travelers and stays in tourist destinations is leading hotels
to be aware of their ecological management and the need for efficient energy
consumption. To achieve this, hotels are increasingly using digitalized systems
and more frequent measurements are made of the variables that affect their
management. Electricity can play a significant role, predicting electricity
usage in hotels, which in turn can enhance their circularity - an approach
aimed at sustainable and efficient resource use. In this study, neural networks
are trained to predict electricity usage patterns in two hotels based on
historical data. The results indicate that the predictions have a good accuracy
level of around 2.5% in MAPE, showing the potential of using these techniques
for electricity forecasting in hotels. Additionally, neural network models can
use climatological data to improve predictions. By accurately forecasting
energy demand, hotels can optimize their energy procurement and usage, moving
energy-intensive activities to off-peak hours to reduce costs and strain on the
grid, assisting in the better integration of renewable energy sources, or
identifying patterns and anomalies in energy consumption, suggesting areas for
efficiency improvements, among other. Hence, by optimizing the allocation of
resources, reducing waste and improving efficiency these models can improve
hotel's circularity.
