Browsing by Author "Granada-Álvarez, Enrique"

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    Assessing the energy demand reduction in a surgical suite by optimizing the HVAC operation during off-use periods
    (Applied Sciences (Switzerland), 2020-03-25) Cacabelos, Antón; López-González, José Luis; González-Gil, Arturo; Febrero-Garrido, Lara; Eguía-Oller, Pablo; Granada-Álvarez, Enrique
    Hospital surgical suites are high consumers of energy due to the strict indoor air quality (IAQ) conditions. However, by varying the ventilation strategies, the potential for energy savings is great, particularly during periods without activity. In addition, there is no international consensus on the ventilation and hygrothermal requirements for surgical areas. In this work, a dynamic energy model of a surgical suite of a Spanish hospital is developed. This energy model is calibrated and validated with experimental data collected during real operation. The model is used to simulate the yearly energy performance of the surgical suite under different ventilation scenarios. The common issue in the studied ventilation strategies is that the hygrothermal conditions ranges are extended during off-use hours. The maximum savings obtained are around 70% of the energy demand without compromising the safety and health of patients and medical staff, as the study complies with current heating, ventilation and air conditioning (HVAC) regulations.
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    Development of a calibrated simulation method for airborne particles to optimize energy consumption in operating rooms
    (Energies, 2019-06-24) Febrero-Garrido, Lara; López-González, José Luis; Eguía-Oller, Pablo; Granada-Álvarez, Enrique
    Operating rooms are stringent controlled environments. All influential factors, in particular, airborne particles, must be within the limits established by regulations. Therefore, energy efficiency stays in the background, prioritizing safety and comfort in surgical areas. However, the potential of improvement in energy savings without compromising this safety is broad. This work presents a new procedure, based on calibrated simulations, that allows the identification of potential energy savings in an operating room, complying with current airborne particle standards. Dynamic energy and airborne particle models are developed and then simulated in TRNSYS and calibrated with GenOpt. The methodology is validated through experimental contrast with a real operating room of a hospital in Spain. A calibrated model with around 2% of error is achieved. The procedure determines the variation in particle concentration according to the flow rate of ventilation supplied and the occupancy of the operating room. In conclusion, energy savings up to 51% are possible, reducing ventilation by 50% while complying with airborne particles standards.
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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.
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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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    Photovoltaic power prediction using artificial neural networks and numerical weather data
    (Sustainability (Switzerland), 2020-12-02) Gómez-López, Javier; Troncoso-Pastoriza, Francisco; Granada-Álvarez, Enrique; Ogando-Martínez, Ana; Febrero-Garrido, Lara; Orosa-García, José Antonio
    The monitoring of power generation installations is key for modelling and predicting their future behaviour. Many renewable energy generation systems, such as photovoltaic panels and wind turbines, strongly depend on weather conditions. However, in situ measurements of relevant weather variables are not always taken into account when designing monitoring systems, and only power output is available. This paper aims to combine data from a Numerical Weather Prediction model with machine learning tools in order to accurately predict the power generation from a photovoltaic system. An Artificial Neural Network (ANN) model is used to predict power outputs from a real installation located in Puglia (southern Italy) using temperature and solar irradiation data taken from the Global Data Assimilation System (GDAS) sflux model outputs. Power outputs and weather monitoring data from the PV installation are used as a reference dataset. Three training and testing scenarios are designed. In the first one, weather data monitoring is used to both train the ANN model and predict power outputs. In the second one, training is done with monitoring data, but GDAS data is used to predict the results. In the last set, both training and result prediction are done by feeding GDAS weather data into the ANN model. The results show that the tested numerical weather model can be combined with machine learning tools to model the output of PV systems with less than 10% error, even when in situ weather measurements are not available.
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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.