Browsing by Author "Martínez-Torres, Javier"

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    Heat loss coefficient estimation applied to existing buildings through machine learning models
    (Applied Sciences (Switzerland), 2020-12-16) Martínez-Comesaña, Miguel; Febrero-Garrido, Lara; Granada-Álvarez, Enrique; Martínez-Torres, Javier; Martínez-Mariño, Sandra
    The Heat Loss Coefficient (HLC) characterizes the envelope efficiency of a building under in-use conditions, and it represents one of the main causes of the performance gap between the building design and its real operation. Accurate estimations of the HLC contribute to optimizing the energy consumption of a building. In this context, the application of black-box models in building energy analysis has been consolidated in recent years. The aim of this paper is to estimate the HLC of an existing building through the prediction of building thermal demands using a methodology based on Machine Learning (ML) models. Specifically, three different ML methods are applied to a public library in the northwest of Spain and compared; eXtreme Gradient Boosting (XGBoost), Support Vector Regression (SVR) and Multi-Layer Perceptron (MLP) neural network. Furthermore, the accuracy of the results is measured, on the one hand, using both CV(RMSE) and Normalized Mean Biased Error (NMBE), as advised by AHSRAE, for thermal demand predictions and, on the other, an absolute error for HLC estimations. The main novelty of this paper lies in the estimation of the HLC of a building considering thermal demand predictions reducing the requirement for monitoring. The results show that the most accurate model is capable of estimating the HLC of the building with an absolute error between 4 and 6%.
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    Optimisation of thermal comfort and indoor air quality estimations applied to in-use buildings combining NSGA-III and XGBoost
    (Sustainable Cities and Society, 2022-05) Martínez-Comesaña, Miguel; Eguía-Oller, Pablo; Martínez-Torres, Javier; Febrero-Garrido, Lara; Granada-Álvarez, Enrique
    Indoor environmental quality (IEQ) monitoring of in-use buildings has become essential in recent years due to the COVID-19 pandemic, as it significantly affects the well-being, health and productivity of building users. Nevertheless, knowing in real time the environmental conditions in large multi-zone areas is a difficult issue. Thus, the use of machine learning techniques to estimate indoor conditions has increased considerably. The aim of this paper is to present an interpolation model, based on an optimised extreme gradient boosting algorithm, to estimate every minute the indoor temperature, relative humidity and CO2 concentration inside buildings. These estimations are obtained without requiring permanent monitoring in the occupied zone. The optimisation, focused on finding the minimum number of monitoring devices needed to provide accurate interpolations, is performed using the multi-objective genetic algorithm NSGA-III. This methodology was applied in a research centre in the north-western Spain. The results show that the optimised or reduced model is capable of estimating indoor temperatures and relative humidity with relative errors below 6% and CO2 levels below 10%.
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    Prediction of building’s thermal performance using LSTM and MLP neural networks
    (Applied Sciences (Switzerland), 2020-11-01) Martínez-Comesaña, Miguel; Febrero-Garrido, Lara; Troncoso-Pastoriza, Francisco; Martínez-Torres, Javier
    Accurate prediction of building indoor temperatures and thermal demand is of great help to control and optimize the energy performance of a building. However, building thermal inertia and lag lead to complex nonlinear systems is difficult to model. In this context, the application of artificial neural networks (ANNs) in buildings has grown considerably in recent years. The aim of this work is to study the thermal inertia of a building by developing an innovative methodology using multi-layered perceptron (MLP) and long short-term memory (LSTM) neural networks. This approach was applied to a public library building located in the north of Spain. A comparison between the prediction errors according to the number of time lags introduced in the models has been carried out. Moreover, the accuracy of the models was measured using the CV(RMSE) as advised by AHSRAE. The main novelty of this work lies in the analysis of the building inertia, through machine learning algorithms, observing the information provided by the input of time lags in the models. The results of the study prove that the best models are those that consider the thermal lag. Errors below 15% for thermal demand and below 2% for indoor temperatures were achieved with the proposed methodology.