# Use of Machine Learning Methods for Indoor Temperature Forecasting
## 2 Methodology and Materials
### Selection of Predictive Models
#### 2.3.1 ML Methods
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Predicted** & **ML** & **Input** & **Data** & **Key** & **Performance** \\  & **Variables** & **Algorithms** & **Variables** & **Source** & **Finding** & **Evaluation** \\ \hline \multirow{4}{*}{[47]} & \multirow{4}{*}{Comfort index} & LR, DT, RF, GB, naive Bayes (NB), Logistic & Indoor & & & \\  & & regression & microenvironment, meteorological & & & & RF model has & \\  & & (LoR) & data, personal & global thermal & & shown better & MSE, R\({}^{2}\) \\  & & ANN, SVM & factors, building & & database & prediction accuracy & \\  & & K-NN & information & & & & accuracy \\  & & adaboost (AB) & & & & & \\ \hline \multirow{4}{*}{[48]} & \multirow{4}{*}{Heating and cooling loads} & \multirow{4}{*}{RF, ET, GB} & \multirow{4}{*}{Building features} & \multirow{4}{*}{12 buildings} & Tree-based & \multirow{4}{*}{} \\  & & & & ensemble & & \\ \cline{1-1}  & & & & learning is able & & \\ \cline{1-1}  & & & & typologies & & & MAPE \\ \cline{1-1}  & & & & & & & \\ \cline{1-1}  & & & & & & & \\ \hline \multirow{4}{*}{[49]} & \multirow{4}{*}{Hourly HVAC energy consumption} & \multirow{4}{*}{ANN, RF} & Meteorological & \multirow{4}{*}{Hotel in Spain} & \multirow{4}{*}{Both models have} & Mean absolute \\  & & & observations, number of guests & & & percentage \\ \cline{1-1}  & & & & & deviation & \\ \cline{1-1}  & & & & & (MAPD), & \\ \cline{1-1}  & & & & data, time of & & & absolute \\ \cline{1-1}  & & & observations, number of guests & & & & (MAD), \\ \cline{1-1}  & & & & & predictive & MAPE, \\ \cline{1-1}  & & & & of rooms booked & & & coefficient of \\ \cline{1-1}  & & & & & & & mean square \\ \cline{1-1}  & & & & & & & (CV-RMSE), R\({}^{2}\) \\ \hline \multirow{4}{*}{[50]} & \multirow{4}{*}{Heating energy consumption} & \multirow{4}{*}{RF, GB} & \multirow{4}{*}{Meteorological data, occupancy data, time of day, historical heating} & XGB exhibits & \multirow{4}{*}{RMSE, MAPE optimal average accuracy} & \multirow{4}{*}{MAE, CV-RMSE} \\  & & & & consumption & & & \\ \cline{1-1}  & & & & & of RF is the & \\ \cline{1-1}  & & & & & highest & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._neurons in the hidden layer. Appendix A summarizes the different tests conducted to estimate the number of neurons and to obtain the best prediction performance. Multiple linear regression (MLR) is a mathematical regression method that extends simple linear regression. It has demonstrated its ability to solve complex problems, in particular a building's energy balance and energy planning [9], daily peak demand and consumption [46], and annual energy consumption [54]. A decision tree (DT) is a technique based on partitioning the dataset into groups in the form of a flowchart. This technique has been widely used in predicting buildings' energy consumption [14; 55] and user comfort indices [47], as well as modeling buildings' energy demands [56]. Ensemble learning has also been applied in monitoring building energy performance, especially bagging and boosting algorithms. Random forest (RF) and extra trees (ET) are representative techniques of the bagging family, which combine a multitude of decision trees. These algorithms have proven their efficiency in predicting a building's cooling and heating loads [48] and energy consumption [49; 57], as well as personal thermal comfort [58; 59]. Gradient boosting (GB) and extreme gradient boosting (XGB) methods also belong to the ensemble learning method. Their basic idea is to combine several simple models called weak learners to obtain a strong model with an improved prediction error. These methods appeared as a promising alternative in the domain of building energy efficiency. Several studies have confirmed their effectiveness in predicting energy consumption [50; 60] and building energy loads [48; 61], establishing predictive energy models [62] as well as detecting faults in HVAC systems [63]. These supervised ML algorithms were selected in this research due to their popularity.