# Use of Machine Learning Methods for Indoor Temperature Forecasting
###### Abstract  
Improving the energy efficiency of the building sector has become an increasing concern in the world, given the alarming reports of greenhouse gas emissions. The management of building energy systems is considered an essential means for achieving this goal. Predicting indoor temperature constitutes a critical task for the management strategies of these systems. Several approaches have been developed for predicting indoor temperature. Determining the most effective has thus become a necessity. This paper contributes to this objective by comparing the ability of seven machine learning algorithms (ML) and the thermal gray box model to predict the indoor temperature of a closed room. The comparison was conducted on a set of data recorded in a room of the Laboratory of Civil Engineering and geo-Environment (LGCgE) at Lille University. The results showed that the best prediction was obtained with the artificial neural network (ANN) and extra trees regressor (ET) methods, which outperformed the thermal gray box model. energy efficiency; prediction; indoor temperature; machine learning; gray box model +
Footnote †: journal: _Future Internet_  
0000-0002-4000-0000-0000-0000-0000-00000-0000-0000-0000-0000-0000-0000-0000-00000-00000-0  
applicability in the modeling of building thermal behavior [12] as well as the forecast of the energy consumption [13; 14; 15] and the energy demand of buildings [16; 17]. Machine learning methods, a branch of artificial intelligence (AI), are considered an effective tool for black-box modeling. In the past decade, these algorithms have experienced remarkable progress [18]. They have offered a promising pathway for the development of prediction models [19]. Scholars have reported their application in the energy prediction of buildings due to their ability to overcome the limits encountered by existing models [20; 21; 22; 23]. Several AI-based techniques have been used to improve buildings' energy performance. The study conducted in [24] tested the capacity of various AI-based algorithms (ANN, fuzzy logic ((FL)), and adaptive neuro-fuzzy inference systems (ANFIS)) to control the thermal conditions and optimize heating loads in double-skin buildings. The study results showed that the selection of the optimal algorithm depends on the objective of the control strategy. ANN was the most suitable energy-saving strategy, while the others were more adapted to thermal comfort strategies. [PERSON] et al. [23] proposed a methodology for the development of an AI-based model for the thermal control of institutional buildings. The results showed that the Gaussian process regression (GPR) outperformed the ANN, support vector machine (SVM), decision tree (DT), and random forest (RF) models. [PERSON] and [PERSON] [25] used two ANN models to predict hourly heating loads using fewer meteorological parameters. [PERSON] et al. [26; 27] applied an SVM-based model to predict the hourly cooling load of an office building. The developed model outperformed the back propagation neural network (BPNN) and the radial basis function neural network (RBFNN) techniques. [PERSON] et al. [22] developed a system based on a multi-layer perception neural network (MLP) to predict energy consumption using data collected from wireless sensors in a two-story building. Compared to four algorithms (linear regression, SVM, RF, and gradient boosting (GB)), the model had improved performance. This model, enhanced with deep learning capabilities, has also outperformed SVM, LR, regression trees (RT), ensemble boosting, and GPR in predicting buildings' aggregated energy demand [28]. In [29] a DT algorithm showed its ability to predict the energy demand of a residential building accurately. [PERSON] et al. [30] demonstrated the superiority of the RF algorithm compared to the RT and the support vector regressor (SVR) methods. [PERSON] et al.