# Use of Machine Learning Methods for Indoor Temperature Forecasting
## 2 Methodology and Materials
### Material  
The study was conducted in an unoccupied closed room in the LGCgE Laboratory at Lille University. The closed room has an area of 9 m\({}^{2}\) and a height of 2.3 m. It is furnished and does not have a facade or windows (Figure 2).  
To model the thermal environment of the room, an intelligent monitoring system composed of a wireless network sensor connected to a micro-computer (Raspberry-pi) was implemented.  
The main objective of these sensors was to track indoor comfort parameters. They provided measurements of four environmental variables: temperature, humidity, luminosity, and noise (THLN). In our work, we focused on the temperature readings. Sensors were installed as shown (Figure 2) on the internal and external faces of the walls of the room and another sensor was suspended at the center to assess the indoor temperature. A standard methodology for monitoring cannot be found in the scientific literature. Therefore, the number of sensors and their positions were based on empirical approaches [40]. However, several studies have developed models to determine the optimal location of sensors to control energy consumption and thermal comfort [41; 42; 43]. In this research, the position of the thermal sensors was determined based on a study carried out in the LGCgE laboratory about the optimal sensor position that can provide representative data of the indoor room environment. Therefore, a sensor was suspended through a wire in the center of the room at a height of 1.5 m above the ground. The position of the sensors recording the temperature of the internal and external faces of the walls was determined based on the manufacturer's recommendations [44]. Two sensors per wall were installed on the internal and external faces of the walls in a neutral zone at the same height above the ground (1.5 m).  
Reliability analysis of the sensors was carried out before their use. A set of sensors was located at the same position. Based on the obtained temperature profiles, these were classified into four groups (Figure 3). The maximum temperature difference between these groups, shown in Figure 4 (0.4 \({}^{\circ}\)C), did not exceed the precision range set at 0.4 \({}^{\circ}\)C. These results confirm the reliability of the sensors and their use in the experiments carried out.  
Figure 2: Reference room in LGCgE laboratory.  
The closed room was heated using a 2000 W power radiator for several hours. The temperatures at the center and on the walls, recording measurements at an interval of 10 min during the experiment, served as a dataset for the applied thermal algorithms.  
Data were checked before their use in numerical modeling for the identification of missing data or abnormal values. Missing data were identified easily since data were recorded at a given time interval. Abnormal values were identified if they exceeded maximum expected values. In these two cases, data were identified and reported as unacceptable data. Since our experiments were conducted in controlled conditions, collected data were exempt from missing data or abnormal values. In the future, techniques based on machine learning will be used to identify and treat missing data and abnormal values.  
The variation of these parameters, as well as the heating period, are illustrated in Figure 5.