Browsing by Author "Pensado-Mariño, Martín"

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    Estimation of heat loss coefficient and thermal demands of in-use building by capturing thermal inertia using lstm neural networks
    (Energies, 2021-08) Pensado-Mariño, Martín; Pérez-Iribarren, Estíbaliz; Granada-Álvarez, Enrique; Febrero-Garrido, Lara; Eguía-Oller, Pablo
    Accurate forecasting of a building thermal performance can help to optimize its energy consumption. In addition, obtaining the Heat Loss Coefficient (HLC) allows characterizing the thermal envelope of the building under conditions of use. The aim of this work is to study the thermal inertia of a building developing a new methodology based on Long Short-Term Memory (LSTM) neural networks. This approach was applied to the Rectorate building of the University of Basque Country (UPV/EHU), located in the north of Spain. A comparison of different time-lags selected to catch the thermal inertia has been carried out using the CV(RMSE) and the MBE errors, as advised by ASHRAE. The main contribution of this work lies in the analysis of thermal inertia detection and its influence on the thermal behavior of the building, obtaining a model capable of predicting the thermal demand with an error between 12 and 21%. Moreover, the viability of LSTM neural networks to estimate the HLC of an in-use building with an error below 4% was demonstrated.
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    Feasibility of different weather data sources applied to building indoor temperature estimation using LSTM neural networks
    (Sustainability (Switzerland), 2021-12-13) Pensado-Mariño, Martín; Febrero-Garrido, Lara; Eguía-Oller, Pablo; Granada-Álvarez, Enrique
    The use of Machine Learning models is becoming increasingly widespread to assess energy performance of a building. In these models, the accuracy of the results depends largely on outdoor conditions. However, getting these data on-site is not always feasible. This article compares the temperature results obtained for an LSTM neural network model, using four types of meteorological data sources. The first is the monitoring carried out in the building; the second is a meteorological station near the site of the building; the third is a table of meteorological data obtained through a kriging process and the fourth is a dataset obtained using GFS. The results are analyzed using the CV(RSME) and NMBE indices. Based on these indices, in the four series, a CV(RSME) slightly higher than 3% is obtained, while the NMBE is below 1%, so it can be deduced that the sources used are interchangeable.