# Use of Machine Learning Methods for Indoor Temperature Forecasting
###### Abstract
[20] demonstrated that ANN is a powerful modeling tool for hourly forecasting of indoor temperature and other thermal parameters in modern buildings one day to one month in advance using only the twelve last indoor and outdoor air temperature and relative humidity values. [PERSON] et al. [31] developed ANN and multiple regression models to predict the daily indoor temperature and relative humidity in an educational building. The study showed that the ANN successfully predicted the thermal parameters and outperformed the multiple regression model. Considering the capacity of the ANN method to deal with time-series data, it has also been used in [32] to predict the indoor temperature in an institutional building. The study highlighted the importance of selecting the relevant input parameters and a suitable training algorithm to improve the prediction results. [PERSON] et al. [33] conducted short-term prediction of indoor temperature using three ML methods (NN, autoregressive models with exogenous inputs (ARX), and extreme learning machine models ((ELM)). The NN model achieved the best prediction. Results also showed that the use of future weather data improves prediction performance. [PERSON] et al. [21] showed that using SVR effectively predicts the indoor temperature of an office building and that it is more accurate than BPNN. The study carried out by [PERSON] et al. [34] confirmed the validity of several linear (autoregressive methods and robust multiple linear regression) and non-linear techniques (ELM and non-linear autoregressive exogenous multilayer perceptron) for predicting indoor temperature. [PERSON] et al. [35] used an approach combining the Internet of Things (IoT) and ML methods to predict the indoor temperature in a smart building. Results showed that the RF, SVM, and NN methods gave accurate results. [PERSON] et al. [36] showed the accuracy of a thermal model based on a DT algorithm using the weather data and occupants' feedback to predict the indoor temperature. The gray box model involves both physical and black-box modeling [37]. This approach is based on the thermal modeling of buildings by analogy with an electrical resistance-capacity circuit [38]. The buildings are modeled by a set of dynamic differential equations representing the phenomenon of conduction, convection, and capacitive phenomenon. Several scholars used this approach in research about building energy efficiency. [PERSON] et al. [17] tested the capacity of four gray box models to predict heating and cooling demands of a multi-zone occupied office building to determine the best model architecture. The results showed that a second-order model was able to well represent the thermal behavior of the office building. [PERSON] et al. [3] developed a hybrid model to predict the average temperature in two-story houses. Tests conducted on the 24 h data horizon gave satisfactory results. [PERSON] et al. [39] created a time-series model to estimate the indoor temperature's real-time cooling load. Two gray box models were combined for the building envelope and the internal thermal mass. The relevance of the model was checked on light, medium, and heavy constructions. A reasonably high degree of precision was obtained for the studied cases. The studies mentioned above focused on using either the machine learning technics or the gray box approach for the thermal building modeling. This paper presents a comparison of the performances of a set of data-driven models in these two categories. The remainder of this paper is organized as follows: Section 2 outlines the research methodology and material; Section 3 presents and discusses the prediction results,, Section 4 summarizes the conclusions and highlights the primary outcome of this research.