# Use of Machine Learning Methods for Indoor Temperature Forecasting
## 4 Conclusions  
Forecasting indoor temperature in buildings constitutes a central task in the optimal energy control in buildings and ensuring comfort and health conditions for users. This prediction combines technical parameters such as building characteristics and their energy system, environmental parameters such as the outdoor temperature and humidity, and social parameters. Considering these techno-social issues in the thermal modeling of buildings requires advanced methods such as machine learning methods. In addition, the consideration of complex building assets and the integration of unstructured data such as those recorded by cameras requires the use of Big Data tools. This paper contributes to the first objective by comparing AI-based technics and a gray box model to predict indoor temperature. This subject is helpful for the assessment of thermal comfort conditions and for reducing energy consumption. The analysis was conducted on temperature datasets collected in a closed room of the LGCgE laboratory at Lille University using MATLAB and python statistical computation language simulations. The adopted models exhibited a favorable prediction capacity in terms of root mean square error (RMSE < 1) and coefficient of determination (R\({}^{2}\) > 0.8). Among these models, ANN and ET emerged as the most suitable algorithms for indoor temperature forecasting, thus surpassing the other ML algorithms and the gray box model. These algorithms were followed by the boosting algorithms that exhibited approximately similar behavior. This research shows that a simple AI-based model could provide accurate forecasting of indoor temperature. It also offers an idea of the effective predictive models to be used in energy management strategies. However, more efforts should be considered in the future to improve the research findings. In addition, this research should be extended to other data collected from other experimentations operating under various conditions with additional parameters such as occupancy and buildings' exposure. The use of these different data will help generalize the results of this research and their use in practical applications. Furthermore, we suggest extending this research to the prediction of the operative temperature. Indeed, although the air temperature is the commonly used parameter in the control of energy systems, the international standards use the operative temperature for the thermal comfort control. **Supplementary Materials:** The supplementary dataset can be seen at [[https://www.mdpi.com/article/10.3390/fi13100242/sl](https://www.mdpi.com/article/10.3390/fi13100242/sl)]([https://www.mdpi.com/article/10.3390/fi13100242/sl](https://www.mdpi.com/article/10.3390/fi13100242/sl)). **Author Contributions:** Conceptualization, L.R., I.S. ; methodology, L.R., I.S. ; validation, L.R., I.S. ; formal analysis L.R. Investigation, L.R. writing--original draft preparation, L.R. writing--review and editing, L.R., I.S. ; supervision, I.S., H.M., F.H.C. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Data Availability Statement:** Not applicable, the study does not report any data. **Conflicts of Interest:** The authors declare no conflict of interest.