Browsing by Author "López-Gómez, Javier"

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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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    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.