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Thermo
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Innovative Technologies to Optimize Building Energy Performance
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Innovative Technologies to Optimize Building Energy Performance

A special issue of Thermo (ISSN 2673-7264).

Deadline for manuscript submissions: 20 April 2025 | Viewed by 6861

Special Issue Editor

Dr. Gerardo Maria Mauro

E-Mail Website
Guest Editor
Department of Engineering, Università degli Studi del Sannio, Piazza Roma 21, 82100 Benevento, Italy
Interests: thermodynamics; modeling of energy systems; energy optimization; energy efficiency; building performance simulation; building optimization; energy retrofit; sustainable design; cost-optimal analysis; energy policies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is well known that building energy optimization and sustainable development are on the same track. The development of new efficient and effective technologies in this sector is a must to fight some critical issues of our generations, such as climatic change, energy poverty, and economic crises. Indeed, the words “sustainable”, “building”, “energy”, “optimization”, and “innovation” provide concepts that need to be combined to promote sustainable development and green economy. New frontiers concerning technologies to reduce the energy intensity of the building sector need to be discovered. 

In this frame, this Special Issue aims to provide a collection of worthy studies concerning

  • Innovative technologies to improve the energy performance of the building envelope;
  • Innovative technologies to reduce the primary energy consumption of building active systems, as concerns both design and operation/control;
  • Innovative integrated technologies concerning envelope and systems to optimize building energy performance.

Original papers related to the above topics and also dealing generally with technologies, methodologies, numerical and experimental investigations, and case studies addressing building energy optimization are welcome.

Thank you for your contributions.

Dr. Gerardo Maria Mauro
Guest Editor

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. Thermo is an international peer-reviewed open access quarterly 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 1000 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

  • innovative building technologies
  • energy optimization
  • sustainability
  • energy efficiency
  • building energy performance
  • building performance simulation
  • building performance optimization
  • building envelope
  • active energy systems
  • renewable energy sources
  • optimization methodologies

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Published Papers (3 papers)

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Research

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15 pages, 3575 KiB  
Article
Heat Exchanger Improvement of a Counter-Flow Dew Point Evaporative Cooler Through COMSOL Simulations
by Mario García-González, Guanggui Cheng, Duc Thuan Bui and Josué Aarón López-Leyva
Thermo 2024, 4(4), 475-489; https://doi.org/10.3390/thermo4040026 - 12 Nov 2024
Viewed by 706
Abstract
Due to modern comfort demands and global warming, heating, ventilation, and air conditioning (HVAC) systems are widely used in many homes and buildings. However, HVAC based on the Vapor Compression System (VCS) is a major energy consumer, accounting for 20–50% of a building’s [...] Read more.
Due to modern comfort demands and global warming, heating, ventilation, and air conditioning (HVAC) systems are widely used in many homes and buildings. However, HVAC based on the Vapor Compression System (VCS) is a major energy consumer, accounting for 20–50% of a building’s energy consumption and responsible for 29% of the world’s CO2 emissions. Dew-point evaporative coolers offer a sustainable alternative yet face challenges, e.g., dew point and wet bulb effectiveness. Given the above, dew point evaporative cooling systems may find a place to dethrone conventional air conditioning systems. This research aims to design a dew point evaporative cooler system with better performance in terms of dew point and wet bulb effectiveness. In terms of methodology, a heat exchanger as part of a counter-flow dew point cooling system was designed and analyzed using COMSOL simulations under different representative climatic, geometric, and dimensional conditions, taking into account turbulent flow. Next, our model was compared with other cooling systems. The results show that our model performs similarly to other cooling systems, with an error of around 6.89% in the output temperature at low relative humidity (0–21%). In comparison, our system is more sensitive to humidity in the climate, whereas heat pumps can operate in high humidity. The average dew point and wet bulb effectiveness were also higher than reported in the literature, at 91.38% and 147.84%, respectively. In addition, there are some potential limitations of the simulations in terms of the assumptions made about atmospheric conditions. For this reason, the results cannot be generalized but must be considered as a starting point for future research and technology development projects. Full article
(This article belongs to the Special Issue Innovative Technologies to Optimize Building Energy Performance)
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21 pages, 2349 KiB  
Article
An Optimized Artificial Neural Network Model of a Limaçon-to-Circular Gas Expander with an Inlet Valve
by Md Shazzad Hossain, Ibrahim Sultan, Truong Phung and Apurv Kumar
Thermo 2024, 4(2), 252-272; https://doi.org/10.3390/thermo4020014 - 11 Jun 2024
Cited by 1 | Viewed by 856
Abstract
In this work, an artificial neural network (ANN)-based model is proposed to describe the input–output relationships in a Limaçon-To-Circular (L2C) gas expander with an inlet valve. The L2C gas expander is a type of energy converter that has great potential to be used [...] Read more.
In this work, an artificial neural network (ANN)-based model is proposed to describe the input–output relationships in a Limaçon-To-Circular (L2C) gas expander with an inlet valve. The L2C gas expander is a type of energy converter that has great potential to be used in organic Rankine cycle (ORC)-based small-scale power plants. The proposed model predicts the different performance indices of a limaçon gas expander for different input pressures, rotor velocities, and valve cutoff angles. A network model is constructed and optimized for different model parameters to achieve the best prediction performance compared to the classic mathematical model of the system. An overall normalized mean square error of 0.0014, coefficient of determination (R2) of 0.98, and mean average error of 0.0114 are reported. This implies that the surrogate model can effectively mimic the actual model with high precision. The model performance is also compared to a linear interpolation (LI) method. It is found that the proposed ANN model predictions are about 96.53% accurate for a given error threshold, compared to about 91.46% accuracy of the LI method. Thus the proposed model can effectively predict different output parameters of a limaçon gas expander such as energy, filling factor, isentropic efficiency, and mass flow for different operating conditions. Of note, the model is only trained by a set of input and target values; thus, the performance of the model is not affected by the internal complex mathematical models of the overall valved-expander system. This neural network-based approach is highly suitable for optimization, as the alternative iterative analysis of the complex analytical model is time-consuming and requires higher computational resources. A similar modeling approach with some modifications could also be utilized to design controllers for these types of systems that are difficult to model mathematically. Full article
(This article belongs to the Special Issue Innovative Technologies to Optimize Building Energy Performance)
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Review

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40 pages, 3950 KiB  
Review
A Review on Machine/Deep Learning Techniques Applied to Building Energy Simulation, Optimization and Management
by Francesca Villano, Gerardo Maria Mauro and Alessia Pedace
Thermo 2024, 4(1), 100-139; https://doi.org/10.3390/thermo4010008 - 6 Mar 2024
Cited by 3 | Viewed by 4373
Abstract
Given the climate change in recent decades and the ever-increasing energy consumption in the building sector, research is widely focused on the green revolution and ecological transition of buildings. In this regard, artificial intelligence can be a precious tool to simulate and optimize [...] Read more.
Given the climate change in recent decades and the ever-increasing energy consumption in the building sector, research is widely focused on the green revolution and ecological transition of buildings. In this regard, artificial intelligence can be a precious tool to simulate and optimize building energy performance, as shown by a plethora of recent studies. Accordingly, this paper provides a review of more than 70 articles from recent years, i.e., mostly from 2018 to 2023, about the applications of machine/deep learning (ML/DL) in forecasting the energy performance of buildings and their simulation/control/optimization. This review was conducted using the SCOPUS database with the keywords “buildings”, “energy”, “machine learning” and “deep learning” and by selecting recent papers addressing the following applications: energy design/retrofit optimization, prediction, control/management of heating/cooling systems and of renewable source systems, and/or fault detection. Notably, this paper discusses the main differences between ML and DL techniques, showing examples of their use in building energy simulation/control/optimization. The main aim is to group the most frequent ML/DL techniques used in the field of building energy performance, highlighting the potentiality and limitations of each one, both fundamental aspects for future studies. The ML approaches considered are decision trees/random forest, naive Bayes, support vector machines, the Kriging method and artificial neural networks. The DL techniques investigated are convolutional and recursive neural networks, long short-term memory and gated recurrent units. Firstly, various ML/DL techniques are explained and divided based on their methodology. Secondly, grouping by the aforementioned applications occurs. It emerges that ML is mostly used in energy efficiency issues while DL in the management of renewable source systems. Full article
(This article belongs to the Special Issue Innovative Technologies to Optimize Building Energy Performance)
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