Prediction of energy consumption in hotels using ANN

TL;DR

This paper demonstrates that neural networks can accurately predict hotel electricity consumption with around 2.5% MAPE, enabling better energy management and supporting sustainable hotel operations.

Contribution

It introduces neural network models that incorporate climatological data to improve hotel energy consumption predictions, advancing digital energy management strategies.

Findings

Neural networks achieved approximately 2.5% MAPE in predictions.

Climatological data enhances prediction accuracy.

Predictions support energy optimization and sustainability in hotels.

Abstract

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.