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Carlos Fernández Bandera
School of Architecture, University of Navarra, 31009 Pamplona, Spain

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Journal article
Published: 10 May 2021 in Sensors
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Accurate load forecasting in buildings plays an important role for grid operators, demand response aggregators, building energy managers, owners, customers, etc. Probabilistic load forecasting (PLF) becomes essential to understand and manage the building’s energy-saving potential. This research explains a methodology to optimize the results of a PLF using a daily characterization of the load forecast. The load forecast provided by a calibrated white-box model and a real weather forecast was classified and hierarchically selected to perform a kernel density estimation (KDE) using only similar days from the database characterized quantitatively and qualitatively. A real case study is presented to show the methodology using an office building located in Pamplona, Spain. The building monitoring, both inside—thermal sensors—and outside—weather station—is key when implementing this PLF optimization technique. The results showed that thanks to this daily characterization, it is possible to optimize the accuracy of the probabilistic load forecasting, reaching values close to 100% in some cases. In addition, the methodology explained is scalable and can be used in the initial stages of its implementation, improving the values obtained daily as the database increases with the information of each new day.

ACS Style

Eva Lucas Segarra; Germán Ramos Ruiz; Carlos Fernández Bandera. Probabilistic Load Forecasting Optimization for Building Energy Models via Day Characterization. Sensors 2021, 21, 3299 .

AMA Style

Eva Lucas Segarra, Germán Ramos Ruiz, Carlos Fernández Bandera. Probabilistic Load Forecasting Optimization for Building Energy Models via Day Characterization. Sensors. 2021; 21 (9):3299.

Chicago/Turabian Style

Eva Lucas Segarra; Germán Ramos Ruiz; Carlos Fernández Bandera. 2021. "Probabilistic Load Forecasting Optimization for Building Energy Models via Day Characterization." Sensors 21, no. 9: 3299.

Journal article
Published: 14 April 2021 in Applied Sciences
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We introduce the Python-based open-source library Energym, a building model library to test and benchmark building controllers. The incorporated building models are presented with a brief explanation of their function, location and technical equipment. Furthermore, the library structure is described, highlighting the necessary features to provide the benchmarking and control capabilities, i.e., standardized evaluation scenarios, key performance indicators (KPIs) and forecasts of uncertain variables. We go on to characterize the evaluation scenarios for each of the models and give formal definitions of the KPIs. We describe the calibration methodologies used for constructing the models and illustrate their usage through examples.

ACS Style

Paul Scharnhorst; Baptiste Schubnel; Carlos Fernández Bandera; Jaume Salom; Paolo Taddeo; Max Boegli; Tomasz Gorecki; Yves Stauffer; Antonis Peppas; Chrysa Politi. Energym: A Building Model Library for Controller Benchmarking. Applied Sciences 2021, 11, 3518 .

AMA Style

Paul Scharnhorst, Baptiste Schubnel, Carlos Fernández Bandera, Jaume Salom, Paolo Taddeo, Max Boegli, Tomasz Gorecki, Yves Stauffer, Antonis Peppas, Chrysa Politi. Energym: A Building Model Library for Controller Benchmarking. Applied Sciences. 2021; 11 (8):3518.

Chicago/Turabian Style

Paul Scharnhorst; Baptiste Schubnel; Carlos Fernández Bandera; Jaume Salom; Paolo Taddeo; Max Boegli; Tomasz Gorecki; Yves Stauffer; Antonis Peppas; Chrysa Politi. 2021. "Energym: A Building Model Library for Controller Benchmarking." Applied Sciences 11, no. 8: 3518.

Journal article
Published: 01 March 2021 in Applied Sciences
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Building information modelling (BIM) is the first step towards the implementation of the industrial revolution 4.0, in which virtual reality and digital twins are key elements. At present, buildings are responsible for 40% of the energy consumption in Europe and, so, there is a growing interest in reducing their energy use. In this context, proper interoperability between BIM and building energy model (BEM) is paramount for integrating the digital world into the construction sector and, therefore, increasing competitiveness by saving costs. This paper evaluates whether there is an automated or semi-automated BIM to BEM workflow that could improve the building design process. For this purpose, a residential building and a warehouse are constructed using the same BIM authoring tool (Revit), where two open schemas were used: green building extensible markup language (gbXML) and industry foundation classes (IFC). These transfer files were imported into software compatible with the EnergyPlus engine—Design Builder, Open Studio, and CYPETHERM HE—in which simulations were performed. Our results showed that the energy models were built up to 7.50% smaller than in the BIM and with missing elements in their thermal envelope. Nevertheless, the materials were properly transferred to gbXML and IFC formats. Moreover, the simulation results revealed a huge difference in values between the models generated by the open schemas, in the range of 6 to 900 times. Overall, we conclude that there exists a semi-automated workflow from BIM to BEM which does not work well for big and complex buildings, as they present major problems when creating the energy model. Furthermore, most of the issues encountered in BEM were errors in the transfer of BIM data to gbXML and IFC files. Therefore, we emphasise the need to improve compatibility between BIM and model exchange formats by their developers, in order to promote BIM–BEM interoperability.

ACS Style

Gabriela Bastos Porsani; Kattalin Del Valle De Lersundi; Ana Sánchez-Ostiz Gutiérrez; Carlos Fernández Bandera. Interoperability between Building Information Modelling (BIM) and Building Energy Model (BEM). Applied Sciences 2021, 11, 2167 .

AMA Style

Gabriela Bastos Porsani, Kattalin Del Valle De Lersundi, Ana Sánchez-Ostiz Gutiérrez, Carlos Fernández Bandera. Interoperability between Building Information Modelling (BIM) and Building Energy Model (BEM). Applied Sciences. 2021; 11 (5):2167.

Chicago/Turabian Style

Gabriela Bastos Porsani; Kattalin Del Valle De Lersundi; Ana Sánchez-Ostiz Gutiérrez; Carlos Fernández Bandera. 2021. "Interoperability between Building Information Modelling (BIM) and Building Energy Model (BEM)." Applied Sciences 11, no. 5: 2167.

Journal article
Published: 23 February 2021 in Energies
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The need to reduce energy consumption in buildings is an urgent task. Increasing the use of calibrated building energy models (BEM) could accelerate this need. The calibration process of these models is a highly under-determined problem that normally yields multiple solutions. Among the uncertainties of calibration, the weather file has a primary position. The objective of this paper is to provide a methodology for selecting the optimal weather file when an on-site weather station with local sensors is available and what is the alternative option when it is not and a mathematically evaluation has to be done with sensors from nearby stations (third-party providers). We provide a quality assessment of models based on the Coefficient of Variation of the Root Mean Square Error (CV(RMSE)) and the Square Pearson Correlation Coefficient ( R 2 ). The research was developed on a control experiment conducted by Annex 58 and a previous calibration study. This is based on the results obtained with the study case based on the data provided by their N2 house.

ACS Style

Vicente Gutiérrez González; Germán Ramos Ruiz; Carlos Fernández Bandera. Impact of Actual Weather Datasets for Calibrating White-Box Building Energy Models Base on Monitored Data. Energies 2021, 14, 1187 .

AMA Style

Vicente Gutiérrez González, Germán Ramos Ruiz, Carlos Fernández Bandera. Impact of Actual Weather Datasets for Calibrating White-Box Building Energy Models Base on Monitored Data. Energies. 2021; 14 (4):1187.

Chicago/Turabian Style

Vicente Gutiérrez González; Germán Ramos Ruiz; Carlos Fernández Bandera. 2021. "Impact of Actual Weather Datasets for Calibrating White-Box Building Energy Models Base on Monitored Data." Energies 14, no. 4: 1187.

Journal article
Published: 01 February 2021 in Sustainability
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The aim of the project detailed in this article was the development of an energy model for verifying Mexican energy standard compliance using the energy simulation engine EnergyPlus through Open Studio SGSAVE software. We aimed to improve the tool’s ability to increase the comfort of social housing through the implementation of the standard in a practical digital tool. The project followed a four-stage methodology. The first stage was the development of climatic zoning for the country. The second stage involved the research and classification of the main traditional construction systems. The third stage was extensive research on the actual state of Mexican energy verification and its legal framework. The standard studied was NOM-020-ENER-2011. The final stage was testing the verification method by introducing the energy Mexican rule into the proposed software with the zoning and construction systems catalogue. A base model of a social housing type was developed in the software. Then, this model was improved to respond to each representative climate zone. Both models were simulated and we verified if they met the requirements. The results were contrasted for determining if there were energy savings. As a conclusion, we found that the actual energy standard of Mexico needs to be changed and we suggest the implementation of the energy simulation engine Energy Plus for creating more complete reports. This will help with the practical improvements in social housing conditions.

ACS Style

Andrés Jonathan Guízar Dena; Miguel Ángel Pascual; Carlos Fernández Bandera. Building Energy Model for Mexican Energy Standard Verification Using Physics-Based Open Studio SGSAVE Software Simulation. Sustainability 2021, 13, 1521 .

AMA Style

Andrés Jonathan Guízar Dena, Miguel Ángel Pascual, Carlos Fernández Bandera. Building Energy Model for Mexican Energy Standard Verification Using Physics-Based Open Studio SGSAVE Software Simulation. Sustainability. 2021; 13 (3):1521.

Chicago/Turabian Style

Andrés Jonathan Guízar Dena; Miguel Ángel Pascual; Carlos Fernández Bandera. 2021. "Building Energy Model for Mexican Energy Standard Verification Using Physics-Based Open Studio SGSAVE Software Simulation." Sustainability 13, no. 3: 1521.

Journal article
Published: 15 November 2020 in Sensors
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In the current energy context of intelligent buildings and smart grids, the use of load forecasting to predict future building energy performance is becoming increasingly relevant. The prediction accuracy is directly influenced by input uncertainties such as the weather forecast, and its impact must be considered. Traditional load forecasting provides a single expected value for the predicted load and cannot properly incorporate the effect of these uncertainties. This research presents a methodology that calculates the probabilistic load forecast while accounting for the inherent uncertainty in forecast weather data. In the recent years, the probabilistic load forecasting approach has increased in importance in the literature but it is mostly focused on black-box models which do not allow performance evaluation of specific components of envelope, HVAC systems, etc. This research fills this gap using a white-box model, a building energy model (BEM) developed in EnergyPlus, to provide the probabilistic load forecast. Through a Gaussian kernel density estimation (KDE), the procedure converts the point load forecast provided by the BEM into a probabilistic load forecast based on historical data, which is provided by the building’s indoor and outdoor monitoring system. An hourly map of the uncertainty of the load forecast due to the weather forecast is generated with different prediction intervals. The map provides an overview of different prediction intervals for each hour, along with the probability that the load forecast error is less than a certain value. This map can then be applied to the forecast load that is provided by the BEM by applying the prediction intervals with their associated probabilities to its outputs. The methodology was implemented and evaluated in a real school building in Denmark. The results show that the percentage of the real values that are covered by the prediction intervals for the testing month is greater than the confidence level (80%), even when a small amount of data are used for the creation of the uncertainty map; therefore, the proposed method is appropriate for predicting the probabilistic expected error in load forecasting due to the use of weather forecast data.

ACS Style

Eva Lucas Segarra; Germán Ramos Ruiz; Carlos Fernández Bandera. Probabilistic Load Forecasting for Building Energy Models. Sensors 2020, 20, 6525 .

AMA Style

Eva Lucas Segarra, Germán Ramos Ruiz, Carlos Fernández Bandera. Probabilistic Load Forecasting for Building Energy Models. Sensors. 2020; 20 (22):6525.

Chicago/Turabian Style

Eva Lucas Segarra; Germán Ramos Ruiz; Carlos Fernández Bandera. 2020. "Probabilistic Load Forecasting for Building Energy Models." Sensors 20, no. 22: 6525.

Journal article
Published: 03 September 2020 in Sensors
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The digital world is spreading to all sectors of the economy, and Industry 4.0, with the digital twin, is a reality in the building sector. Energy reduction and decarbonization in buildings are urgently required. Models are the base for prediction and preparedness for uncertainty. Building energy models have been a growing field for a long time. This paper proposes a novel calibration methodology for a building energy model based on two pillars: simplicity, because there is an important reduction in the number of parameters (four) to be adjusted, and cost-effectiveness, because the methodology minimizes the number of sensors provided to perform the process by 47.5%. The new methodology was validated empirically and comparatively based on a previous work carried out in Annex 58 of the International Energy Agency (IEA). The use of a tested and structured experiment adds value to the results obtained.

ACS Style

Vicente Gutiérrez González; Germán Ramos Ruiz; Carlos Fernández Bandera. Empirical and Comparative Validation for a Building Energy Model Calibration Methodology. Sensors 2020, 20, 5003 .

AMA Style

Vicente Gutiérrez González, Germán Ramos Ruiz, Carlos Fernández Bandera. Empirical and Comparative Validation for a Building Energy Model Calibration Methodology. Sensors. 2020; 20 (17):5003.

Chicago/Turabian Style

Vicente Gutiérrez González; Germán Ramos Ruiz; Carlos Fernández Bandera. 2020. "Empirical and Comparative Validation for a Building Energy Model Calibration Methodology." Sensors 20, no. 17: 5003.

Journal article
Published: 21 August 2020 in Sustainability
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The use of building energy models (BEMs) is becoming increasingly widespread for assessing the suitability of energy strategies in building environments. The accuracy of the results depends not only on the fit of the energy model used, but also on the required external files, and the weather file is one of the most important. One of the sources for obtaining meteorological data for a certain period of time is through an on-site weather station; however, this is not always available due to the high costs and maintenance. This paper shows a methodology to analyze the impact on the simulation results when using an on-site weather station and the weather data calculated by a third-party provider with the purpose of studying if the data provided by the third-party can be used instead of the measured weather data. The methodology consists of three comparison analyses: weather data, energy demand, and indoor temperature. It is applied to four actual test sites located in three different locations. The energy study is analyzed at six different temporal resolutions in order to quantify how the variation in the energy demand increases as the time resolution decreases. The results showed differences up to 38% between annual and hourly time resolutions. Thanks to a sensitivity analysis, the influence of each weather parameter on the energy demand is studied, and which sensors are worth installing in an on-site weather station are determined. In these test sites, the wind speed and outdoor temperature were the most influential weather parameters.

ACS Style

Eva Segarra; Germán Ruiz; Vicente González; Antonis Peppas; Carlos Bandera. Impact Assessment for Building Energy Models Using Observed vs. Third-Party Weather Data Sets. Sustainability 2020, 12, 6788 .

AMA Style

Eva Segarra, Germán Ruiz, Vicente González, Antonis Peppas, Carlos Bandera. Impact Assessment for Building Energy Models Using Observed vs. Third-Party Weather Data Sets. Sustainability. 2020; 12 (17):6788.

Chicago/Turabian Style

Eva Segarra; Germán Ruiz; Vicente González; Antonis Peppas; Carlos Bandera. 2020. "Impact Assessment for Building Energy Models Using Observed vs. Third-Party Weather Data Sets." Sustainability 12, no. 17: 6788.

Journal article
Published: 16 January 2020 in Sustainability
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There is a growing concern about how to mitigate climate change, in which the production and use of energy has a great impact as one of the largest sources of global greenhouse gases (GHG). Buildings are responsible for a large percentage of these emissions. Therefore, there has been an increase in research in this area, in order to reduce their consumption and increase their efficiency. One of the major simulation programs used in optimization research is EnergyPlus. The purpose of this software is the complete energy simulation of a building, although it lacks tools to analyze its results and, above all, to manage and edit its simulations. For this reason, we developed an application programming interface (API) that serves to merge two areas which are highly demanded by researchers: energy building simulation (using EnergyPlus) and tools for the management and design of research experiments (in this case, MATLAB®). The developed API allows the user to perform complex simulations using EnergyPlus in a simple way, as it allows the editing of each simulation and the analysis of the simulation results through MATLAB®. In addition, it enables the user to simultaneously run multiple simulations, using either all computer core processors or a selection of them (i.e., allowing parallel computing), reducing the simulation time. The API was developed in the C# language, such that it can be used with any software that can import . N E T libraries.

ACS Style

Germán Campos Gordillo; Germán Ramos Ruiz; Yves Stauffer; Stephan Dasen; Carlos Fernández Bandera. EplusLauncher: An API to Perform Complex EnergyPlus Simulations in MATLAB® and C#. Sustainability 2020, 12, 672 .

AMA Style

Germán Campos Gordillo, Germán Ramos Ruiz, Yves Stauffer, Stephan Dasen, Carlos Fernández Bandera. EplusLauncher: An API to Perform Complex EnergyPlus Simulations in MATLAB® and C#. Sustainability. 2020; 12 (2):672.

Chicago/Turabian Style

Germán Campos Gordillo; Germán Ramos Ruiz; Yves Stauffer; Stephan Dasen; Carlos Fernández Bandera. 2020. "EplusLauncher: An API to Perform Complex EnergyPlus Simulations in MATLAB® and C#." Sustainability 12, no. 2: 672.

Journal article
Published: 11 January 2020 in Sustainability
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The self-consumption without surplus to the grid is one of the aspects of the new Spanish law for prosumers. Increasing the share of renewable energy sources into the grid inherently leads to several constraints. The mismatch between the energy demand and the renewable energy production, which is intermittent in nature, is one of those challenges. Storage offers the possibility to decouple demand and supply, and therefore, it adds flexibility to the electric system. This research evaluates expanding electricity self-consumption without surplus to the grid by harnessing thermal mass storage in the residential sector. The methodology is investigated by using a variable refrigerant flow air conditioner system. Because there is no option to export the excess capacity to the grid, this research proposes an approach to profiting from this surplus energy by activating structural thermal mass, which is quantified from the information acquired using a building energy model. For this purpose, an EnergyPlus model of a flat in Pamplona (Spain) was used. The optimization analysis was based on a set-point modulation control strategy. Results show that under adequate climatological circumstances, the proposed methodology can reduce the total electric energy from the grid between by 60– 80 % .

ACS Style

Carlos Fernández Bandera; Jose Pachano; Jaume Salom; Antonis Peppas; Germán Ramos Ruiz. Photovoltaic Plant Optimization to Leverage Electric Self Consumption by Harnessing Building Thermal Mass. Sustainability 2020, 12, 553 .

AMA Style

Carlos Fernández Bandera, Jose Pachano, Jaume Salom, Antonis Peppas, Germán Ramos Ruiz. Photovoltaic Plant Optimization to Leverage Electric Self Consumption by Harnessing Building Thermal Mass. Sustainability. 2020; 12 (2):553.

Chicago/Turabian Style

Carlos Fernández Bandera; Jose Pachano; Jaume Salom; Antonis Peppas; Germán Ramos Ruiz. 2020. "Photovoltaic Plant Optimization to Leverage Electric Self Consumption by Harnessing Building Thermal Mass." Sustainability 12, no. 2: 553.

Journal article
Published: 31 May 2019 in Energies
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Building Energy Models (BEMs) are a key element of the Energy Performance of Buildings Directive (EPBD), and they are at the basis of Energy Performance Certificates (EPCs). The main goal of BEMs is to provide information for building stakeholders; they can be a powerful market tool to increase demand for energy efficiency solutions in buildings without affecting the comfort of users, as well as providing other benefits. The next generation of BEMs should value buildings in a holistic and cost-effective manner across several complementary dimensions: envelope performances, system performances, and controlling the ability of buildings to offer flexible services to the grid by optimizing energy consumption, distributed generation, and storage. SABINA is a European project that aims to look for flexibility to the grid, targeting the most economic source possible: existing thermal inertia in buildings. In doing so, SABINA works with a new generation of BEMs that tend to mimic the thermal behavior of real buildings and therefore requires an accurate methodology to choose the model that complies with the requirements of the system. This paper details our novel extensive research on which statistical indices should be chosen in order to identify the best model offered by the calibration process developed by Fernandez et al. in a previous paper and therefore is a continuation of that work.

ACS Style

Vicente Gutiérrez González; Lissette Álvarez Colmenares; Jesús Fernando López Fidalgo; Germán Ramos Ruiz; Carlos Fernández Bandera. Uncertainy’s Indices Assessment for Calibrated Energy Models. Energies 2019, 12, 2096 .

AMA Style

Vicente Gutiérrez González, Lissette Álvarez Colmenares, Jesús Fernando López Fidalgo, Germán Ramos Ruiz, Carlos Fernández Bandera. Uncertainy’s Indices Assessment for Calibrated Energy Models. Energies. 2019; 12 (11):2096.

Chicago/Turabian Style

Vicente Gutiérrez González; Lissette Álvarez Colmenares; Jesús Fernando López Fidalgo; Germán Ramos Ruiz; Carlos Fernández Bandera. 2019. "Uncertainy’s Indices Assessment for Calibrated Energy Models." Energies 12, no. 11: 2096.

Journal article
Published: 05 April 2019 in Energies
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The use of Building Energy Models (BEM) has become widespread to reduce building energy consumption. Projection of the model in the future to know how different consumption strategies can be evaluated is one of the main applications of BEM. Many energy management optimization strategies can be used and, among others, model predictive control (MPC) has become very popular nowadays. When using models for predicting the future, we have to assume certain errors that come from uncertainty parameters. One of these uncertainties is the weather forecast needed to predict the building behavior in the near future. This paper proposes a methodology for quantifying the impact of the error generated by the weather forecast in the building’s indoor climate conditions and energy demand. The objective is to estimate the error introduced by the weather forecast in the load forecasting to have more precise predicted data. The methodology employed site-specific, near-future forecast weather data obtained through online open access Application Programming Interfaces (APIs). The weather forecast providers supply forecasts up to 10 days ahead of key weather parameters such as outdoor temperature, relative humidity, wind speed and wind direction. This approach uses calibrated EnergyPlus models to foresee the errors in the indoor thermal behavior and energy demand caused by the increasing day-ahead weather forecasts. A case study investigated the impact of using up to 7-day weather forecasts on mean indoor temperature and energy demand predictions in a building located in Pamplona, Spain. The main novel concepts in this paper are: first, the characterization of the weather forecast error for a specific weather data provider and location and its effect in the building’s load prediction. The error is calculated based on recorded hourly data so the results are provided on an hourly basis, avoiding the cancel out effect when a wider period of time is analyzed. The second is the classification and analysis of the data hour-by-hour to provide an estimate error for each hour of the day generating a map of hourly errors. This application becomes necessary when the building takes part in the day-ahead programs such as demand response or flexibility strategies, where the predicted hourly load must be provided to the grid in advance. The methodology developed in this paper can be extrapolated to any weather forecast provider, location or building.

ACS Style

Eva Lucas Segarra; Hu Du; Germán Ramos Ruiz; Carlos Fernández Bandera. Methodology for the Quantification of the Impact of Weather Forecasts in Predictive Simulation Models. Energies 2019, 12, 1309 .

AMA Style

Eva Lucas Segarra, Hu Du, Germán Ramos Ruiz, Carlos Fernández Bandera. Methodology for the Quantification of the Impact of Weather Forecasts in Predictive Simulation Models. Energies. 2019; 12 (7):1309.

Chicago/Turabian Style

Eva Lucas Segarra; Hu Du; Germán Ramos Ruiz; Carlos Fernández Bandera. 2019. "Methodology for the Quantification of the Impact of Weather Forecasts in Predictive Simulation Models." Energies 12, no. 7: 1309.

Journal article
Published: 23 December 2018 in Energies
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There is growing concern about how to mitigate climate change in which the reduction of CO2 emissions plays an important role. Buildings have gained attention in recent years since they are responsible for around 30% of greenhouse gases. In this context, advance control strategies to optimize HVAC systems are necessary because they can provide significant energy savings whilst maintaining indoor thermal comfort. Simulation-based model predictive control (MPC) procedures allow an increase in building energy performance through the smart control of HVAC systems. The paper presents a methodology that overcomes one of the critical issues in using detailed building energy models in MPC optimizations—computational time. Through a case study, the methodology explains how to resolve this issue. Three main novel approaches are developed: a reduction in the search space for the genetic algorithm (NSGA-II) thanks to the use of the curve of free oscillation; a reduction in convergence time based on a process of two linked stages; and, finally, a methodology to measure, in a combined way, the temporal convergence of the algorithm and the precision of the obtained solution.

ACS Style

Germán Ramos Ruiz; Eva Lucas Segarra; Carlos Fernández Bandera. Model Predictive Control Optimization via Genetic Algorithm Using a Detailed Building Energy Model. Energies 2018, 12, 34 .

AMA Style

Germán Ramos Ruiz, Eva Lucas Segarra, Carlos Fernández Bandera. Model Predictive Control Optimization via Genetic Algorithm Using a Detailed Building Energy Model. Energies. 2018; 12 (1):34.

Chicago/Turabian Style

Germán Ramos Ruiz; Eva Lucas Segarra; Carlos Fernández Bandera. 2018. "Model Predictive Control Optimization via Genetic Algorithm Using a Detailed Building Energy Model." Energies 12, no. 1: 34.

Journal article
Published: 13 November 2018 in Energies
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This study presents a novel optimization methodology for choosing optimal building retrofitting strategies based on the concept of exergy analysis. The study demonstrates that the building exergy analysis may open new opportunities in the design of an optimal retrofit solution despite being a theoretical approach based on the high performance of a Carnot reverse cycle. This exergy-based solution is different from the one selected through traditional efficient retrofits where minimizing energy consumption is the primary selection criteria. The new solution connects the building with the reference environment, which acts as “an unlimited sink or unlimited sources of energy”, and it adapts the building to maximize the intake of energy resources from the reference environment. The building hosting the School of Architecture at the University of Navarra has been chosen as the case study building. The unique architectural appearance and bespoke architectural characteristics of the building limit the choices of retrofitting solutions; therefore, retrofitting solutions on the façade, roof, roof skylight and windows are considered in multi-objective optimization using the jEPlus package. It is remarkable that different retrofitting solutions have been obtained for energy-driven and exergy-driven optimization, respectively. Considering the local contexts and all possible reference environments for the building, three “unlimited sinks or unlimited sources of energy” are selected for the case study building to explore exergy-driven optimization: the external air, the ground in the surrounding area and the nearby river. The evidence shows that no matter which reference environment is chosen, an identical envelope retrofitting solution has been obtained.

ACS Style

Carlos Fernández Bandera; Ana Fei Muñoz Mardones; Hu Du; Juan Echevarría Trueba; Germán Ramos Ruiz. Exergy As a Measure of Sustainable Retrofitting of Buildings. Energies 2018, 11, 3139 .

AMA Style

Carlos Fernández Bandera, Ana Fei Muñoz Mardones, Hu Du, Juan Echevarría Trueba, Germán Ramos Ruiz. Exergy As a Measure of Sustainable Retrofitting of Buildings. Energies. 2018; 11 (11):3139.

Chicago/Turabian Style

Carlos Fernández Bandera; Ana Fei Muñoz Mardones; Hu Du; Juan Echevarría Trueba; Germán Ramos Ruiz. 2018. "Exergy As a Measure of Sustainable Retrofitting of Buildings." Energies 11, no. 11: 3139.

Journal article
Published: 11 December 2017 in Energies
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Building energy performance (BEP) is an ongoing point of reflection among researchers and practitioners. The importance of buildings as one of the largest activators in climate change mitigation was illustrated recently at the United Nations Framework Convention on Climate Change 21st Conference of the Parties (COP21). Continuous technological improvements make it necessary to revise the methodology for energy calculations in buildings, as has recently happened with the new international standard ISO 52016-1 on Energy Performance of Buildings. In this area, there is a growing need for advanced tools like building energy models (BEMs). BEMs should play an important role in this process, but until now there has no been international consensus on how these models should reconcile the gap between measurement and simulated data in order to make them more reliable and affordable. Our proposal is a new generation of models that reconcile the traditional data-driven (inverse) modelling and law-driven (forward) modelling in a single type that we have called law-data-driven models. This achievement has greatly simplified past methodologies, and is a step forward in the search for a standard in the process of calibrating a building energy model.

ACS Style

Carlos Fernández Bandera; Germán Ramos Ruiz. Towards a New Generation of Building Envelope Calibration. Energies 2017, 10, 2102 .

AMA Style

Carlos Fernández Bandera, Germán Ramos Ruiz. Towards a New Generation of Building Envelope Calibration. Energies. 2017; 10 (12):2102.

Chicago/Turabian Style

Carlos Fernández Bandera; Germán Ramos Ruiz. 2017. "Towards a New Generation of Building Envelope Calibration." Energies 10, no. 12: 2102.

Journal article
Published: 12 October 2017 in Energies
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Nowadays, there is growing interest in all the smart technologies that provide us with information and knowledge about the human environment. In the energy field, thanks to the amount of data received from smart meters and devices and the progress made in both energy software and computers, the quality of energy models is gradually improving and, hence, also the suitability of Energy Conservation Measures (ECMs). For this reason, the measurement of the accuracy of building energy models is an important task, because once the model is validated through a calibration procedure, it can be used, for example, to apply and study different strategies to reduce its energy consumption in maintaining human comfort. There are several agencies that have developed guidelines and methodologies to establish a measure of the accuracy of these models, and the most widely recognized are: ASHRAE Guideline 14-2014, the International Performance Measurement and Verification Protocol (IPMVP) and the Federal Energy Management Program (FEMP). This article intends to shed light on these validation measurements (uncertainty indices) by focusing on the typical mistakes made, as these errors could produce a false belief that the models used are calibrated.

ACS Style

Germán Ramos Ruiz; Carlos Fernández Bandera. Validation of Calibrated Energy Models: Common Errors. Energies 2017, 10, 1587 .

AMA Style

Germán Ramos Ruiz, Carlos Fernández Bandera. Validation of Calibrated Energy Models: Common Errors. Energies. 2017; 10 (10):1587.

Chicago/Turabian Style

Germán Ramos Ruiz; Carlos Fernández Bandera. 2017. "Validation of Calibrated Energy Models: Common Errors." Energies 10, no. 10: 1587.

Journal article
Published: 01 April 2016 in Applied Energy
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ACS Style

Germán Ramos Ruiz; Carlos Fernández Bandera; Tomás Gómez-Acebo Temes; Ana Sánchez-Ostiz Gutierrez. Genetic algorithm for building envelope calibration. Applied Energy 2016, 168, 691 -705.

AMA Style

Germán Ramos Ruiz, Carlos Fernández Bandera, Tomás Gómez-Acebo Temes, Ana Sánchez-Ostiz Gutierrez. Genetic algorithm for building envelope calibration. Applied Energy. 2016; 168 ():691-705.

Chicago/Turabian Style

Germán Ramos Ruiz; Carlos Fernández Bandera; Tomás Gómez-Acebo Temes; Ana Sánchez-Ostiz Gutierrez. 2016. "Genetic algorithm for building envelope calibration." Applied Energy 168, no. : 691-705.