Browsing by Author "Martínez-Comesaña, Miguel"

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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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    IoT-based platform for automated IEQ spatio-temporal analysis in buildings using machine learning techniques
    (Automation in Construction, 2022-07) Troncoso-Pastoriza, Francisco; Martínez-Comesaña, Miguel; Ogando-Martínez, Ana; López-Gómez, Javier; Eguía-Oller, Pablo; Febrero-Garrido, Lara
    Providing accurate information about the indoor environmental quality (IEQ) conditions inside building spaces is essential to assess the comfort levels of their occupants. These values may vary inside the same space, especially for large zones, requiring many sensors to produce a fine-grained representation of the space conditions, which increases hardware installation and maintenance costs. However, sound interpolation techniques may produce accurate values with fewer input points, reducing the number of sensors needed. This work presents a platform to automate this accurate IEQ representation based on a few sensor devices placed across a large building space. A case study is presented in a research centre in Spain using 8 wall-mounted devices and an additional moving device to train a machine learning model. The system yields accurate results for estimations at positions and times never seen before by the trained model, with relative errors between 4% and 10% for the analysed variables.
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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.
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    Use of optimised MLP neural networks for spatiotemporal estimation of indoor environmental conditions of existing buildings
    (Building and Environment, 2021-11) Martínez-Comesaña, Miguel; Ogando-Martínez, Ana; Troncoso-Pastoriza, Francisco; López-Gómez, Javier; Febrero-Garrido, Lara; Granada-Álvarez, Enrique
    Controlling the indoor environmental quality in real time is essential for the health, well-being and productivity of occupants of a building. In recent years, research has focused on improving monitoring devices and strategies and developing techniques for estimating indoor conditions. The use of machine learning algorithms in this context has increased considerably. However, monitoring data in real time from large multizone working areas is challenging. The aim of this work is to provide an interpolation methodology based on the use of optimised multilayered perceptron neural networks to estimate the indoor environmental conditions of a building in real time. These estimations are obtained without the need for neither monitoring in the occupied working area nor human intervention and considering low-cost sensors. The neural network is optimised by implementing the multiobjective genetic algorithm NSGA-II to find the best architecture in terms of error and complexity. This method was applied to the building of a research centre in north-western Spain, where interpolated values for indoor air temperature, relative humidity and CO2 concentration were obtained. The results of this case study yielded relative errors close to 6% for temperature, 5% for relative humidity, and 12% for CO2 concentration. These values validate the methodology developed for the estimation of indoor environmental conditions and the contribution of this research to the improvement of the monitoring and control of the indoor environmental quality of a building.