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Applied Sciences
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Energy Efficiency and Thermal Comfort in Buildings
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Energy Efficiency and Thermal Comfort in Buildings

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: closed (20 October 2024) | Viewed by 3118

Special Issue Editor

Prof. Dr. Rafael Royo-Pastor

E-Mail Website
Guest Editor
Escuela Técnica Superior de Ingeniería Industrial, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: architectural engineering; heat transfer; building energy efficiency

Special Issue Information

Dear Colleagues,

In recent years, in the face of the climate crisis and the necessary reduction in energy consumption, different international organizations, such as the European Commission, have underlined the important role that buildings play in this indicator. Buildings are a very important opportunity to reduce the energy consumed by our society, while other sectors, such as industry or transport, present much greater difficulties. It is important to point out that we currently possess the technology to achieve this reduction in consumption without a reduction in thermal comfort, and achieving this binomial (a reduction in energy consumption and an increase in thermal comfort simultaneously) is the most important issue. Nowadays, this is possible, and it is the focus of this Special Issue, in which any type of research is welcome that rigorously contributes to these objectives.

Energy efficiency has been shown to be a fundamental tool, not only in the construction materials involved (insulation, phase change material, reduction in density, or increase in stored energy as intended), but also in the components used for climatization. It is necessary to highlight the use of heat pumps for which energy is taken from the environment that is considered most appropriate, renewable, and economical.

Any additional considerations will be welcomed since the width of the issue allows any additional contribution!

Prof. Dr. Rafael Royo-Pastor
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

  • advanced building materials
  • infrared thermography
  • energy efficiency
  • heat pumps
  • zero-energy buildings
  • phase change materials
  • U-value

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Published Papers (5 papers)

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Research

28 pages, 3067 KiB  
Article
Triple Validation of Calibrated Building Energy Models with Different Air Infiltration Values
by Gabriela Bastos Porsani, Juan Bautista Echeverría Trueba and Carlos Fernández Bandera
Appl. Sci. 2024, 14(23), 10828; https://doi.org/10.3390/app142310828 - 22 Nov 2024
Viewed by 315
Abstract
Model calibration refines design-stage inputs to align with real-world building performance. Accurate parameter selection, especially for highly sensitive variables like air leakage, is crucial. This study compared two building energy model calibration methods. The “classic” method adjusted indoor air capacitance, internal mass, and [...] Read more.
Model calibration refines design-stage inputs to align with real-world building performance. Accurate parameter selection, especially for highly sensitive variables like air leakage, is crucial. This study compared two building energy model calibration methods. The “classic” method adjusted indoor air capacitance, internal mass, and air infiltration, while a novel method focused on capacitance and internal mass, using empirical data for infiltration. The infiltration values were calculated using the decay equation and the EnergyPlus equations with site-specific coefficients. A triple validation assessed model performance in terms of temperature (CIBSE TM63), energy consumption (minimization), and indoor air quality (represented by CO2 levels in accordance with the ASTM D5157 Standard). Results demonstrated the novel method’s superiority across all three performance metrics. All calibrated models met the CIBSE TM63 criteria even during the validation period, which was five times longer than the training period. Compared to the classic method, models incorporating dynamic empirical infiltration showed a 29% and 26% improvement in MAE and RMSE, respectively, in temperature prediction. In energy consumption results, the novel method models presented a 31% reduction, and for CO2 level agreement, these models achieved a 130% higher R2 value than the classic model. In addition, the classic method’s infiltration values failed to meet ASTM D5157 requirements, suggesting reliance on unrealistic parameter values for accurate temperature representation. The incorporation of calculated air leakage data into the BEM allowed a more realistic estimation of capacitance and internal mass values, emphasizing the importance of accurate air infiltration modeling for overall model reliability. Full article
(This article belongs to the Special Issue Energy Efficiency and Thermal Comfort in Buildings)
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Figure 1
<p>Model calibration process flowchart.</p> Full article ">Figure 2
<p>Apartment floor plan with the location of the sensors used in this study. To facilitate understanding of the sensor locations in the living room, we divided the space into 8 sections: A and B represent the horizontal division down the middle of the room, while 1, 2, 3, and 4 are vertical divisions.</p> Full article ">Figure 3
<p>Daily indoor dry-bulb temperature for the period studied from 10 February 2023 until 26 April 2023.</p> Full article ">Figure 4
<p>Indoor <math display="inline"><semantics> <msub> <mi>CO</mi> <mn>2</mn> </msub> </semantics></math> concentration, along with outdoor <math display="inline"><semantics> <msub> <mi>CO</mi> <mn>2</mn> </msub> </semantics></math> concentration, for each decay day in the training period.</p> Full article ">Figure 5
<p>Indoor <math display="inline"><semantics> <msub> <mi>CO</mi> <mn>2</mn> </msub> </semantics></math> concentration, along with outdoor <math display="inline"><semantics> <msub> <mi>CO</mi> <mn>2</mn> </msub> </semantics></math> concentration, for each decay day in the checking period.</p> Full article ">Figure 6
<p>Representation of the BEM created in DesignBuilder, along with a floor plan showing the nine thermal zones. The case study area is outlined in yellow.</p> Full article ">Figure 7
<p>Flowchart of the triple validation steps for model evaluation.</p> Full article ">Figure 8
<p>Scatter plots of measured and simulated temperature values for baseline, classic, decay, and ELA REG models during the training (<b>a</b>) and checking (<b>b</b>) periods.</p> Full article ">Figure 9
<p>Total energy consumption of the four building energy models during the training (<b>a</b>) and checking (<b>b</b>) periods.</p> Full article ">Figure 10
<p>Correlation graphs of measured and predicted <math display="inline"><semantics> <msub> <mi>CO</mi> <mn>2</mn> </msub> </semantics></math> curves for baseline, classic, and ELA REG models during the training (<b>a</b>) and checking (<b>b</b>) periods.</p> Full article ">Figure 11
<p>Summary of the results of this study.</p> Full article ">Figure A1
<p>Total energy consumption in training (<b>a</b>) and checking (<b>b</b>) periods.</p> Full article ">
21 pages, 9713 KiB  
Article
CFD Study of the Impact of an Electrical Power Transformer on a Historical Building: Assessment and Solutions
by Fabio Nardecchia, Luca Gugliermetti, Laura Pompei and Federico Cinquepalmi
Appl. Sci. 2024, 14(23), 10827; https://doi.org/10.3390/app142310827 - 22 Nov 2024
Viewed by 376
Abstract
Historical building reuse is aimed at preservation, where buildings are recovered for new uses connected to cultural activities. This paper presents the analysis of the impact of thermo-fluid dynamics due to a 500 kW electrical power transformer installed inside a historical building. The [...] Read more.
Historical building reuse is aimed at preservation, where buildings are recovered for new uses connected to cultural activities. This paper presents the analysis of the impact of thermo-fluid dynamics due to a 500 kW electrical power transformer installed inside a historical building. The analysis is performed using computational fluid dynamics simulations validated through measurement campaigns carried out during the summer period. High temperatures and wide humidity variations can damage building plasters and cause malfunctions in power equipment. To avoid these situations, two different installation layouts were studied. One consists of the power transformer directly installed in the environment and cooled by an inlet fan, and the other consists of the power transformer being insulated from the external environment by an enclosure connected to a forced ventilation system. The second layout showed better results both inside and outside the transformer enclosure. The maximum indoor condition was about 4.3 °C, with a −7.2% RH and an airflow rate of 1100 m3/h, and the maximum outdoor air condition was 3.3 °C, with a −1.39% RH and a flow rate of 2200 m3/h. However, the temperatures and humidity inside the building and outside the transformer enclosure were almost the same. Full article
(This article belongs to the Special Issue Energy Efficiency and Thermal Comfort in Buildings)
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Figure 1

Figure 1
<p>Museéto architectural plan with three sections at the beginning, middle and end of the room.</p> Full article ">Figure 2
<p>Environmental data (Temperature [°C], Relative Humidity [%RH] and Dew Point [°C]) collected by the weather station during the period from 24 July 2020 to 15 September 2020.</p> Full article ">Figure 3
<p>Solar radiation [W/m<sup>2</sup>] measured by the weather station during the period from 24 July 2020 to 15 September 2020.</p> Full article ">Figure 4
<p>(<b>a</b>) Wind Rose chart with principal wind components (in [km/h]) measured by the weather station during the period from 24 July 2020 to 15 September 2020; (<b>b</b>) Average wind speed components (in [km/h]) measured by the weather station during the period from 24 July 2020 to 15 September 2020.</p> Full article ">Figure 5
<p>Domain mesh and sensor locations.</p> Full article ">Figure 6
<p>SENSOR 66: comparisons of temperature (<b>left</b>) and relative humidity (<b>right</b>) measured by the sensor, simulated, and registered outside the building.</p> Full article ">Figure 7
<p>SENSOR 72: comparisons of temperature (<b>left</b>) and relative humidity (<b>right</b>) measured by the sensor, simulated, and registered outside the building.</p> Full article ">Figure 8
<p>SENSOR 84: comparison of temperature (<b>left</b>) and relative humidity (<b>right</b>) measured by the sensor, simulated, and registered outside the building.</p> Full article ">Figure 9
<p>Temperature (<b>left</b>) and relative humidity (<b>right</b>) results on the longitudinal section.</p> Full article ">Figure 10
<p>(<b>a</b>) Technical system of air extraction in layout one (open transformer enclosure); (<b>b</b>) layout of the open transformer enclosure.</p> Full article ">Figure 11
<p>(<b>a</b>) Layout of the open transformer enclosure; (<b>b</b>) layout of the confined transformer enclosure.</p> Full article ">Figure 12
<p>Simulation O-1, Sensor 66: temperature (<b>left</b>) and relative humidity (<b>right</b>) results.</p> Full article ">Figure 13
<p>Simulation O-1: Temperature and relative humidity fields on the lateral plane.</p> Full article ">Figure 14
<p>Simulation O-2, Sensor 66: temperature (<b>left</b>) and relative humidity (<b>right</b>) results.</p> Full article ">Figure 15
<p>Simulation O-2: Temperature (<b>left</b>) and relative humidity (<b>right</b>) fields on the longitudinal plane.</p> Full article ">Figure 16
<p>Simulation C-1: Temperature (<b>left</b>) and relative humidity (<b>right</b>) fields on the longitudinal plan.</p> Full article ">Figure 17
<p>Simulation C-4: Temperature (<b>left</b>) and relative humidity (<b>right</b>) fields on the longitudinal plan.</p> Full article ">
22 pages, 1159 KiB  
Article
Energetic Analysis of Passive Solar Strategies for Residential Buildings with Extreme Summer Conditions
by Stephanny Nogueira, Ana I. Palmero-Marrero, David Borge-Diez, Emin Açikkalp and Armando C. Oliveira
Appl. Sci. 2024, 14(22), 10761; https://doi.org/10.3390/app142210761 - 20 Nov 2024
Viewed by 455
Abstract
This study investigates the implementation of passive design strategies to improve the thermal environment in the extremely hot climates of Brazil, Portugal, and Turkey. Given the rising cooling demands due to climate change, optimizing energy efficiency in buildings is essential. Using the Trace [...] Read more.
This study investigates the implementation of passive design strategies to improve the thermal environment in the extremely hot climates of Brazil, Portugal, and Turkey. Given the rising cooling demands due to climate change, optimizing energy efficiency in buildings is essential. Using the Trace 3D Plus v6.00.106 software, typical residential buildings for each country were simulated to assess various passive solutions, such as building orientation, wall and roof modifications, glazing optimization options, window-to-wall ratio (WTWR) reduction, shading, and natural ventilation. The findings highlight that Brazil experienced the higher discomfort temperatures compared to Mediterranean climates, with indoor air temperatures exceeding 28 °C all year round and remaining between 34 °C and 37 °C for nearly 40% of the time. Building orientation had a minimal impact near the equator, while Mediterranean climates benefited from an up to 10% variation in energy demand. Thermal insulation combined with white exterior paint resulted in Şanlıurfa experiencing annual energy savings of up to 26%. Optimal roof solutions yielded a 19% demand reduction in Évora, while WTWR reduction and double-colored glazing achieved up to a 35% reduction in Évora and 19% in other regions. Combined strategies achieved energy demand reductions of 44% for Évora, 40% for Şanlıurfa, and 32% for Teresina. The study emphasizes the need for integrated, climate-specific passive solutions, showing their potential to enhance both energy efficiency and the thermal environment in residential buildings across diverse hot climates. Full article
(This article belongs to the Special Issue Energy Efficiency and Thermal Comfort in Buildings)
►▼ Show Figures

Figure 1

Figure 1
<p>House plan for Évora, Portugal.</p> Full article ">Figure 2
<p>House plan for Teresina, Brazil.</p> Full article ">Figure 3
<p>House plan for Şanlıurfa, Turkey.</p> Full article ">Figure 4
<p>A 3D view of the house for Şanlıurfa, Turkey.</p> Full article ">Figure 5
<p>True north rotation from plan north.</p> Full article ">Figure 6
<p>Results of the base simulations for each location. (<b>a</b>) Thermal load and annual energy demand. (<b>b</b>) Annual indoor air temperature distribution with no AS.</p> Full article ">Figure 7
<p>Effect of combined passive solutions on energy needs and indoor air temperature for house with horizontal roof of Teresina, Brazil. (<b>a</b>) Annual energy. (<b>b</b>) Annual indoor air temperature distribution.</p> Full article ">Figure 8
<p>Effect of combined passive solutions on energy needs and indoor air temperature for house with inclined roof and concrete slab of Teresina, Brazil. (<b>a</b>) Annual energy. (<b>b</b>) Annual indoor air temperature distribution.</p> Full article ">Figure 9
<p>Effect of combined passive solutions on energy needs and indoor air temperature for house with inclined roof and gypsum board of Teresina, Brazil. (<b>a</b>) Annual energy. (<b>b</b>) Annual indoor air temperature distribution.</p> Full article ">Figure 10
<p>Effect of combined passive solutions on energy needs and indoor air temperature for house with horizontal roof of Évora, Portugal. (<b>a</b>) Annual energy. (<b>b</b>) Annual indoor air temperature distribution.</p> Full article ">Figure 11
<p>Effect of combined passive solutions on energy needs and indoor air temperature for house with inclined roof and concrete slab of Évora, Portugal. (<b>a</b>) Annual energy. (<b>b</b>) Annual indoor air temperature distribution.</p> Full article ">Figure 12
<p>Effect of combined passive solutions on energy needs and indoor air temperature for house with inclined roof and gypsum board of Évora, Portugal. (<b>a</b>) Annual energy. (<b>b</b>) Annual indoor air temperature distribution.</p> Full article ">Figure 13
<p>Effect of combined passive solutions on energy needs and indoor air temperature for house with horizontal roof of Şanlıurfa, Turkey. (<b>a</b>) Annual energy. (<b>b</b>) Annual indoor air temperature distribution.</p> Full article ">Figure 14
<p>Effect of combined passive solutions on energy needs and indoor air temperature for house with inclined roof and concrete slab of Şanlıurfa, Turkey. (<b>a</b>) Annual energy. (<b>b</b>) Annual indoor air temperature distribution.</p> Full article ">Figure 15
<p>Effect of combined passive solutions on energy needs and indoor air temperature for house with inclined roof and gypsum board of Şanlıurfa, Turkey. (<b>a</b>) Annual energy. (<b>b</b>) Annual indoor air temperature distribution.</p> Full article ">
24 pages, 5211 KiB  
Article
Sustainable Building Tool by Energy Baseline: Case Study
by Rosaura Castrillón-Mendoza, Javier M. Rey-Hernández, Larry Castrillón-Mendoza and Francisco J. Rey-Martínez
Appl. Sci. 2024, 14(20), 9403; https://doi.org/10.3390/app14209403 - 15 Oct 2024
Viewed by 722
Abstract
This study explores innovative methodologies for estimating the energy baseline (EnBL) of a university classroom building, emphasizing the critical roles of data quality and model selection in achieving accurate energy efficiency assessments. We compare time series models that are suitable for buildings with [...] Read more.
This study explores innovative methodologies for estimating the energy baseline (EnBL) of a university classroom building, emphasizing the critical roles of data quality and model selection in achieving accurate energy efficiency assessments. We compare time series models that are suitable for buildings with limited consumption data with univariate and multivariate regression models that incorporate additional variables, such as weather and occupancy. Furthermore, we investigate the advantages of dynamic simulation using the EnergyPlus engine (V5, USDOE United States) and Design Builder software v7, enabling scenario analysis for various operational conditions. Through a comprehensive case study at the UAO University Campus, we validate our models using daily monitoring data and statistical analysis in RStudio. Our findings reveal that model choice significantly influences energy consumption forecasts, leading to potential overestimations or underestimations of savings. By rigorously assessing statistical validation and error analysis results, we highlight the implications for decarbonization strategies in building design and operation. This research provides a valuable framework for selecting appropriate methodologies for energy baseline estimation, enhancing transparency and reliability in energy performance assessments. These contributions are particularly relevant for optimizing energy use and aligning with regulatory requirements in the pursuit of sustainable building practices. Full article
(This article belongs to the Special Issue Energy Efficiency and Thermal Comfort in Buildings)
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Figure 1

Figure 1
<p>Base period concept and reference period concept for IDE.</p> Full article ">Figure 2
<p>Methodology for LBE model selection.</p> Full article ">Figure 3
<p>Lecture hall building of a university campus.</p> Full article ">Figure 4
<p>Views of the building, modeled with Design Builder. (<b>a</b>) Southwest view. (<b>b</b>) Northeast view.</p> Full article ">Figure 5
<p>Monthly electricity consumption of the lecture hall building of the UAO University campus.</p> Full article ">Figure 6
<p>Seasonal performance of final energy consumption of the lecture hall building.</p> Full article ">Figure 7
<p>Results of the different forecasting methods to obtain an annual LBEn for the building.</p> Full article ">Figure 8
<p>Total final energy consumption correlation with independent variables—RStudio software.</p> Full article ">Figure 9
<p>EnBL of classroom energy consumption (kWh/month) vs. total occupancy (hours).</p> Full article ">Figure 10
<p>Simulated vs. monitored energy consumption.</p> Full article ">Figure 11
<p>Electrical consumption breakdown of the building. Devices: 84,630.14 kWh, Lighting 10,014.03 kWh, Refrigeration: 37,466.96 kWh, Outdoor lighting: 1735.83 kWh.</p> Full article ">Figure 12
<p>CO<sub>2</sub> emissions for the lecture hall building.</p> Full article ">
35 pages, 8530 KiB  
Article
Development and Application of a Novel Non-Iterative Balancing Method for Hydronic Systems
by Federico Pedranzini, Luigi P. M. Colombo and Francesco Romano
Appl. Sci. 2024, 14(14), 6232; https://doi.org/10.3390/app14146232 - 17 Jul 2024
Viewed by 838
Abstract
The improvement of efficiency in new and existing buildings is one of the key aspects in achieving the climate change targets promoted by international regulatory and technical bodies, and among the measures that deserve renewed attention is the balancing of hydronic systems. However, [...] Read more.
The improvement of efficiency in new and existing buildings is one of the key aspects in achieving the climate change targets promoted by international regulatory and technical bodies, and among the measures that deserve renewed attention is the balancing of hydronic systems. However, the balancing procedures currently applied have not been updated for decades and are still largely unimplemented, as they are mainly based on cumbersome and iterative procedures. This paper deals with the proposal and advanced adaptation of a non-iterative balancing method previously developed for air systems, known as the progressive flow method (PFM). The application to water systems of the PFM’s concepts includes some aspects of an existing empirical method called the compensated method (CM) and overcomes its main limitations; moreover, the original PFM has been radically rethought in its implementation aspects, taking advantage of the tightness of water distribution systems, minimising instrumentation and the number of measurement operations, to definitively overcome the iterative nature of the currently applied methods. Experimental validation was carried out. Compared with a standard method, the enhanced PFM reduced the number of measurements by 48% and the number of balancing operations by 41%, achieving final flow rates within tolerances and the same configuration of balancing devices. Full article
(This article belongs to the Special Issue Energy Efficiency and Thermal Comfort in Buildings)
►▼ Show Figures

Figure 1

Figure 1
<p>Compensated method procedure performed by an operator; different levels of gray indicate sequential operations, darker means more recent.</p> Full article ">Figure 2
<p>Equivalence of graphical representation air vs. water system: both can be represented by the same lumped-parameters model.</p> Full article ">Figure 3
<p>Representation of a hydronic distribution system at the generic shunt point.</p> Full article ">Figure 4
<p>Lumped-parameters representation of a hydronic distribution system at the farthest terminal.</p> Full article ">Figure 5
<p>Lumped-parameters representation of the downstream part of the circuit; for clarity, the real configuration on the left and its equivalent configuration on the right are enclosed within the dashed rectangular shapes.</p> Full article ">Figure 6
<p>Insertion of a line-balancing device downstream of the disadvantaged terminal, lumped-parameters model.</p> Full article ">Figure 7
<p>Insertion of a line-balancing device downstream of the disadvantaged terminal, system equipment representation.</p> Full article ">Figure 8
<p>Representation of the system to be balanced, balancing devices, and required measuring points/instruments.</p> Full article ">Figure 9
<p>First step of PFM procedure; tuning flow rate of the farthest terminal by adjusting pump velocity.</p> Full article ">Figure 10
<p>Implementing the reference pressure loop control (second step), then acting in sequence on terminal valves from n − 1 to 1 (third step).</p> Full article ">Figure 11
<p>Configuration of the system to be balanced.</p> Full article ">Figure 12
<p>Test rig realization.</p> Full article ">Figure 13
<p>Final valve configurations after application of ratio and PF methods.</p> Full article ">Figure 14
<p>Behavior of flow rate variation vs. valve opening in a real system at different authority percentages.</p> Full article ">Figure A1
<p>typical manifold- based local user system.</p> Full article ">Figure A2
<p>Combined primary distribution supplying local manifold-based user systems with direct and reverse return configuration.</p> Full article ">Figure A3
<p>PFM procedure application in case of manifold-based system or sub-system.</p> Full article ">Figure A4
<p>system composed by a main manifold serving more risers each supplying local manifolds.</p> Full article ">Figure A5
<p>configuration of the system to be balanced.</p> Full article ">Figure A6
<p>Test rig configuration and components.</p> Full article ">Figure A7
<p>Rotameter flow meters, with bypass branch for maintenance operations.</p> Full article ">Figure A8
<p>Test rig construction drawing.</p> Full article ">Figure A9
<p>Ratio Method TAB sequence; testing and terminal pair balancing.</p> Full article ">Figure A10
<p>Ratio Method TAB sequence; testing and branches balancing.</p> Full article ">Figure A11
<p>PFM TAB sequence; testing and terminal balancing.</p> Full article ">Figure A12
<p>PFM TAB sequence; testing and branches balancing.</p> Full article ">
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