Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
3.2
Journals
Energies
Special Issues
Improvements in Building Thermal and Energy Behaviours towards Net Zero...
energies-logo
Submit to Energies Review for Energies Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Improvements in Building Thermal and Energy Behaviours towards Net Zero Performance

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "G: Energy and Buildings".

Deadline for manuscript submissions: closed (19 April 2024) | Viewed by 4278

Special Issue Editors

Dr. Miroslava Kavgic

E-Mail Website
Guest Editor
Civil Engineering Department, University of Ottawa, 161 Louis Pasteur, Ottawa, ON K1N 6N5, Canada
Interests: zero-carbon building systems; building energy consumption; low-carbon building envelopes; indoor environmental quality; renewable energy technologies
Special Issues, Collections and Topics in MDPI journals
Dr. Abdulhameed Babatunde Owolabi

E-Mail Website
Guest Editor
Regional Leading Research Center for Smart Energy Systems, School of Convergence & Fusion Systems Engineering, Kyungpook National University, Sangju 37224, Republic of Korea
Interests: energy modeling optimization; renewable energy planning; energy transition; energy decarbonization; energy management; techno-economic analysis; on-grid and off-grid modeling; renewable energy technologies; energy efficiency
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue encourages contributions presenting the latest research results and good examples of building technologies that can drive the transition towards net-zero performance buildings based on experimental studies and numerical simulation. Both original research papers and review papers are welcome. Topics of interest include, but are not limited to:

  • Bioclimatic building design;
  • Envelope materials, components, and systems from low-carbon building materials;
  • Super-insulating and innovative materials (e.g., vacuum insulation panels, gas-filled panels, aerogel-based products, and phase change materials);
  • High-performance heating and ventilation systems for cold climates;
  • Passive solar design for heating-dominated climates;
  • Renewable energy systems for buildings;
  • Advanced control strategies for building systems;
  • Indoor environmental quality in high-performance buildings;
  • Advancements in thermal and moisture control in building envelopes;
  • Retrofitting for optimal energy performance;
  • Natural daylight, efficient lighting, and low-energy appliances in high-performance buildings.

Dr. Miroslava Kavgic
Dr. Abdulhameed Babatunde Owolabi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • zero-carbon buildings
  • HVAC systems
  • renewable energy technologies
  • low-carbon building materials
  • advanced control strategies
  • indoor environmental quality
  • advanced building materials
  • passive solar design
  • bioclimatic design
  • envelope hygrothermal performance
  • energy-efficient retrofit

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

23 pages, 6519 KiB  
Article
Precision Leak Detection in Supermarket Refrigeration Systems Integrating Categorical Gradient Boosting with Advanced Thresholding
by Rashinda Wijethunga, Hooman Nouraei, Craig Zych, Jagath Samarabandu and Ayan Sadhu
Energies 2024, 17(3), 736; https://doi.org/10.3390/en17030736 - 4 Feb 2024
Viewed by 699
Abstract
Supermarket refrigeration systems are integral to food security and the global economy. Their massive scale, characterized by numerous evaporators, remote condensers, miles of intricate piping, and high working pressure, frequently leads to problematic leaks. Such leaks can have severe consequences, impacting not only [...] Read more.
Supermarket refrigeration systems are integral to food security and the global economy. Their massive scale, characterized by numerous evaporators, remote condensers, miles of intricate piping, and high working pressure, frequently leads to problematic leaks. Such leaks can have severe consequences, impacting not only the profits of the supermarkets, but also the environment. With the advent of Industry 4.0 and machine learning techniques, data-driven automatic fault detection and diagnosis methods are becoming increasingly popular in managing supermarket refrigeration systems. This paper presents a novel leak-detection framework, explicitly designed for supermarket refrigeration systems. This framework is capable of identifying both slow and catastrophic leaks, each exhibiting unique behaviours. A noteworthy feature of the proposed solution is its independence from the refrigerant level in the receiver, which is a common dependency in many existing solutions for leak detection. Instead, it focuses on parameters that are universally present in supermarket refrigeration systems. The approach utilizes the categorical gradient boosting regression model and a thresholding algorithm, focusing on features that are sensitive to leaks as target features. These include the coefficient of performance, subcooling temperature, superheat temperature, mass flow rate, compression ratio, and energy consumption. In the case of slow leaks, only the coefficient of performance shows a response. However, for catastrophic leaks, all parameters except energy consumption demonstrate responses. This method detects slow leaks with an average F1 score of 0.92 within five days of occurrence. The catastrophic leak detection yields F1 scores of 0.7200 for the coefficient of performance, 1.0000 for the subcooling temperature, 0.4118 for the superheat temperature, 0.6957 for the mass flow rate, and 0.8824 for the compression ratio, respectively. Full article
(This article belongs to the Special Issue Improvements in Building Thermal and Energy Behaviours towards Net Zero Performance)
►▼ Show Figures

Figure 1

12 pages, 7970 KiB  
Article
Machine Learning-Based Indoor Relative Humidity and CO2 Identification Using a Piecewise Autoregressive Exogenous Model: A Cob Prototype Study
by Mohammed-Hichem Benzaama, Karim Touati, Yassine El Mendili, Malo Le Guern, François Streiff and Steve Goodhew
Energies 2024, 17(1), 243; https://doi.org/10.3390/en17010243 - 3 Jan 2024
Viewed by 925
Abstract
The population of developed nations spends a significant amount of time indoors, and the implications of poor indoor air quality (IAQ) on human health are substantial. Many premature deaths attributed to exposure to indoor air pollutants result from diseases exacerbated by poor indoor [...] Read more.
The population of developed nations spends a significant amount of time indoors, and the implications of poor indoor air quality (IAQ) on human health are substantial. Many premature deaths attributed to exposure to indoor air pollutants result from diseases exacerbated by poor indoor air. CO2, one of these pollutants, is the most prevalent and often serves as an indicator of IAQ. Indoor CO2 concentrations can be significantly higher than outdoor levels due to human respiration and activity. The primary objective of this research was to numerically investigate the indoor relative humidity and CO2 in cob buildings through the CobBauge prototype, particularly during the first months following the building delivery. Both in situ experimental studies and numerical predictions using an artificial neural network were conducted for this purpose. The study presented the use of a piecewise autoregressive exogenous model (PWARX) for indoor relative humidity (RH) and CO2 content in a building constructed with a double walling system consisting of cob and light earth. The model was validated using experimental data collected over a 27-day period, during which indoor RH and CO2 levels were measured alongside external conditions. The results indicate that the PWARX model accurately predicted RH levels and categorized them into distinct states based on moisture content within materials and external conditions. However, while the model accurately predicted indoor CO2 levels, it faced challenges in finely classifying them due to the complex interplay of factors influencing CO2 levels in indoor environments. Full article
(This article belongs to the Special Issue Improvements in Building Thermal and Energy Behaviours towards Net Zero Performance)
►▼ Show Figures

Figure 1

24 pages, 4685 KiB  
Article
MEVO: A Metamodel-Based Evolutionary Optimizer for Building Energy Optimization
by Rafael Batres, Yasaman Dadras, Farzad Mostafazadeh and Miroslava Kavgic
Energies 2023, 16(20), 7026; https://doi.org/10.3390/en16207026 - 10 Oct 2023
Cited by 1 | Viewed by 1150
Abstract
A deep energy retrofit of building envelopes is a vital strategy to reduce final energy use in existing buildings towards their net-zero emissions performance. Building energy modeling is a reliable technique that provides a pathway to analyze and optimize various energy-efficient building envelope [...] Read more.
A deep energy retrofit of building envelopes is a vital strategy to reduce final energy use in existing buildings towards their net-zero emissions performance. Building energy modeling is a reliable technique that provides a pathway to analyze and optimize various energy-efficient building envelope measures. However, conventional optimization analyses are time-consuming and computationally expensive, especially for complex buildings and many optimization parameters. Therefore, this paper proposed a novel optimization algorithm, MEVO (metamodel-based evolutionary optimizer), developed to efficiently identify optimal retrofit solutions for building envelopes while minimizing the need for extensive simulations. The key innovation of MEVO lies in its integration of evolutionary techniques with design-of-computer experiments, machine learning, and metaheuristic optimization. This approach continuously refined a machine learning model through metaheuristic optimization, crossover, and mutation operations. Comparative assessments were conducted against four alternative metaheuristic algorithms and Bayesian optimization, demonstrating MEVO’s effectiveness in reliably finding the best solution within a reduced computation time. A hypothesis test revealed that the proposed algorithm is significantly better than Bayesian optimization in finding the best cost values. Regarding computation time, the proposed algorithm is 4–7 times faster than the particle swarm optimization algorithm and has a similar computational speed as Bayesian Optimization. Full article
(This article belongs to the Special Issue Improvements in Building Thermal and Energy Behaviours towards Net Zero Performance)
►▼ Show Figures

Figure 1

13 pages, 10112 KiB  
Article
Airtightness Assessment under Several Low-Pressure Differences in Non-Residential Buildings
by Chanhyung Shim and Goopyo Hong
Energies 2023, 16(19), 6845; https://doi.org/10.3390/en16196845 - 27 Sep 2023
Viewed by 764
Abstract
The thermal performance of building envelopes is significantly affected by building insulation and airtightness. However, most studies have focused on improving thermal performance in building envelopes, while few studies on improving airtightness in buildings have been conducted. The present study measured airtightness and [...] Read more.
The thermal performance of building envelopes is significantly affected by building insulation and airtightness. However, most studies have focused on improving thermal performance in building envelopes, while few studies on improving airtightness in buildings have been conducted. The present study measured airtightness and infiltration in non-residential buildings using fan pressurization and tracer gas methods. By analyzing the results obtained from both methods, the distribution of the correlation factors was identified, which can be used for the air leakage rates obtained from the blower door test to estimate the infiltration rates under natural airflow conditions. Since it is difficult to get the values of ACH50 through the blower door test in buildings of large volume or where large air leakages occur, the study proposed a method to convert the values of airtightness under several low-pressure differences of 20 Pa, 25 Pa, 30 Pa and 35 Pa into ACH50 using conversion coefficient. By dividing the air leakage rate under 20 Pa pressure difference by the conversion coefficient of 0.60, the values of ACH50 can be estimated. Results converted to ACH50 using conversion coefficient for various pressure differences of 20 Pa, 25 Pa, 30 Pa, and 35 Pa showed an error of 0.1–4.4%, respectively, compared to actual ACH50 measurement results. Full article
(This article belongs to the Special Issue Improvements in Building Thermal and Energy Behaviours towards Net Zero Performance)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop