Thermo

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

Published Papers (2 papers)

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Research

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21 pages, 2349 KiB

Open AccessArticle

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

Viewed by 146

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 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 (

R^{2}

) 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

Open AccessReview

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 1 | Viewed by 1713

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 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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