# Prediction of Building's Thermal Performance Using LSTM and MLP Neural Networks
## 4 Conclusions  
A new application of machine and deep learning algorithms and a new methodology to analyze the influence of the thermal inertia of a building are presented in this paper. The study was conducted by analyzing monitored data of thermal demand and indoor temperatures of the Science Library of the University of Vigo. The data were completed with weather variables (outdoor temperatures and solar radiation) and three temporal variables (hour of the day, day of the week and hour of the year). The aim of this analysis is to study the importance of the thermal inertia of the building in predicting thermal demand and indoor temperatures. The methodology used in this study is based, on the one hand, on a classic machine learning model (MLP neural network) contrasting the improvement in the prediction accuracy provided by the thermal inertia of the building. On the other hand, as a comparison, a deep learning model (LSTM neural network) was carried out through an analogous analysis. The research contribution of this paper is the application of mathematical methods to evaluate the influence of the thermal inertia of the building in the prediction errors of the thermal demand and the indoor temperatures of the building itself. The principal limitation of this research is the analysis of the thermal inertia of the building based on the difference in the errors rates of indoor temperatures depending on the number of time lags. The stable behavior of the indoor temperatures by default, produce that, in general, the prediction error is very low. This makes analysis and detection of the additional information that can provide the introduction of time lags in the models more difficult. On the other hand, the presented methods contribute with advantages over the already existing \begin{table}
\begin{tabular}{c|c c c c|c c c c} \hline
**LSTM Model** & \multicolumn{2}{c}{**Sample1**} & \multicolumn{2}{c}{**Sample2**} & \multicolumn{2}{c}{**MLP Model**} & \multicolumn{2}{c}{**Sample1**} & \multicolumn{2}{c}{**Sample2**} \\  & CV(RMSE) & SD & CV(RMSE) & SD & & CV(RMSE) & SD & CV(RMSE) & SD \\ \hline No lags & - & - & - & - & No lags (5-5-1) & 1.82\% & 0.001 & 2.21\% & \(\approx\) 0 \\
1 lag (10-10-0.5-5-1) & 1.89\% & 0.002 & 2.05\% & 0.001 & 1 lag (5-5-5-1) & 1.86\% & 0.001 & 2.03\% & 0.003 \\
6 lags (5-5-22-1) & 2.40\% & 0.003 & 2.23\% & 0.004 & 6 lags (4-4-4-1) & 2.06\% & 0.001 & 2.28\% & 0.003 \\
12 logs (5-5-2-1) & 2.42\% & 0.002 & 2.19\% & 0.005 & 12 lags (6-6-6-1) & 2.02\% & \(\approx\) 0 & 1.90\% & \(\approx\) 0 \\
24 lags (5-5-2-1) & 2.68\% & 0.004 & 2.10\% & 0.004 & 24 lags (12-12-12-1) & 1.73\% & \(\approx\) 0 & 2.01\% & 0.001 \\ \hline \end{tabular}
\end{table} 
Table 3: Results of the indoor temperatures predictions based on the time lags introduced in the neural network model. LSTM errors are presented in the first three columns, whereas MLP errors are presented in the last three. The results shown summarize the 10 repetitions of the experiment, for each architecture and each sample analyzed. The mean of the CV(RMSE) obtained in each of them and the standard deviation (SD) are presented. The different structures of the neural networks that provide the best accuracy are presented between brackets. research in building simulation and thermal inertia analysis of buildings. With traditional building simulations models, both to make predictions and to analyze the thermal inertia of the building, it is necessary to have a deep knowledge about the specific subject and control many different energy parameters. With the methods presented in this paper, known as black box models, it is possible to do all kinds of analyses without any specific knowledge, and also to do it faster than dynamic simulation methods. These models rely on having a significant amount of data to be able to extract information from it and extrapolate it. Additionally, the variables involved in this study are commonly monitored in numerous buildings.