# Heat Loss Coefficient Estimation Applied to Existing Buildings through Machine Learning Models
## 4 Conclusions  
A new methodology for estimating the HLC of a building is presented in this paper. It is based on the introduction of thermal demand predictions obtained with machine learning models in the HLC formula. This study focuses on the analysis, on the one hand, of monitored data on heating demands and indoor temperatures belonging to the Science Library of the University of Vigo. On the other hand, two meteorological variables (outdoor temperature and solar radiation) and three temporal variables (hour of the year, day of the week and hour of the day) are also taken into account. The aim of this paper is to show a methodology that allows the efficient estimation of the HLC value of a building without the need to control its heating demands (nor indoor temperatures if they are assumed to be constant). The search for the optimal methodology considers and compares three different machine learning models (XGBoost, SVR and MLP neural network). In addition, the performance of each one is evaluated and analysed both through its average accuracy in thermal demand predictions and its average accuracy in HLC estimations. The research contribution of this work is the application of mathematical models to estimate the HLC of a building with low errors. In addition to reducing the necessity for monitoring, these models can be useful for detecting errors in measurements from sensors installed in the building. Moreover, the black-box models presented contributes with advantages compared to traditional research in building simulation. The use and application of the traditional building thermal simulation models are conditioned by the need for significant knowledge on a subject. In addition, these models need to control many different parameters related to the energy performance of a building. Nevertheless, machine learning models, which need less time for development than dynamic simulation methods, do not require specific prior knowledge and can be applied in numerous fields. The only important need for these models is the availability of a significant amount of data from which a behaviour pattern is extracted. Furthermore, the inputs introduced in the built models are variables typically monitored in buildings. Therefore, the methodology presented here is extractable to other studies and buildings. The results obtained show that it is possible to efficiently estimate the HLC value of a building over specific time periods with black-box models. To this end, it is important to obtain thermal demand predictions, which are used as inputs in HLC estimations, with average errors lower than the values proposed for the calibrated models. The results also demonstrate that the MLP neural network is the algorithm with the best average performance in HLC estimation (more stability and higher average accuracy in all samples studied). The SVR model shows a close average behaviour, but XGBoost, except in Sample 1, presents a much worse performance than the other two. In the first validation sample, in which the measured HLC is 2.750 kW/K, only the MPL neural network is capable of obtaining an average absolute variation rate below 10% (6.50%). The SVR and XGBoost models obtain an average variation of 12.16% and 12.52%, respectively. On the other hand, in the second validation sample, where the calculated HLC was 2.146 kW/K, both the MLP and SVR models performed better than XGBoost. While the first two obtain an average absolute variation rate of 4.08% and 5.68%, respectively, XGBoost shows an average variation of 8.40%. Lastly, in the case of the third validation sample, with a measured HLC value of 2.464 kW/K, the MLP neural network yields an average absolute variation rate far from the other algorithms (4.97%). The SVR and XGBoost models are only able to yield an average absolute variation of 13.60% and 23.05%, respectively. Furthermore, regarding the dispersion in error data, MLP model is the one that shows the most stable results in all validation samples.