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Advances in Artificial Intelligence and Machine Learning Applied to Energy Efficiency in Building Design

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

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 9565

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Special Issue Editor

Dr. Hossein Moayedi

E-Mail Website1 Website2 Website3
Guest Editor

Faculty of Civil Engineering, Duy Tan University, Da Nang 550000, Vietnam
Interests: geotechnical engineering; artificial intelligence; GIS; machine learning; energy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

To simulate an energy-efficient design model for buildings, two criteria that should be comprehensively addressed are sustainability and viability. Different modeling can present numerous possibilities and provide multiple socioeconomic and environmental benefits. Various difficulties associated with these simulations in building design when it comes to providing an efficient building design (e.g., variable behavior for different conditions, incorporation of several parameters in determining a specific parameter, etc.) necessitate using the most state-of-the-art techniques. Many artificial intelligence models, such as artificial neural networks, fuzzy-based networks, and support vector machines, have been effectively applied to energy efficiency in building design engineering problems. The performance of artificial intelligence models has frequently come out to be superior to traditional approaches. The present Special Issue welcomes scientific papers that deal with the application of recent artificial intelligence techniques in problems dealing with energy efficiency in building design engineering. The authors are encouraged to submit their studies within the scope of “Advances in Artificial Intelligence and Machine Learning Applied to energy efficiency in building design.” Novel deep learning algorithms and metaheuristic-optimized ensembles can be of high interest. Disseminating the solutions in the form of a graphical user interface (GUI) and mathematical equations is recommended to enable the readers to make practical use.

Focal points of this Special Issue include, but are not limited to, innovative applications of intelligent models in:

Energy efficiency;
Building design;
Smart buildings;
Modeling of advanced materials and technologies in buildings;
Geohazards and disasters prevention related to building design;
Pollutions and emissions;
Modeling of advanced materials and technologies in buildings;
Geomechanics for energy and environment.

Dr. Hossein Moayedi
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. 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.

Published Papers (5 papers)

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Research

29 pages, 6311 KiB

Open AccessArticle

I²OT-EC: A Framework for Smart Real-Time Monitoring and Controlling Crude Oil Production Exploiting IIOT and Edge Computing

by Hazem Ramzey, Mahmoud Badawy, Mostafa Elhosseini and Adel A. Elbaset

Energies 2023, 16(4), 2023; https://doi.org/10.3390/en16042023 - 18 Feb 2023

Cited by 7 | Viewed by 2658

Abstract

The oil and gas business has high operating costs and frequently has significant difficulties due to asset, process, and operational failures. Remote monitoring and management of the oil field operations are essential to ensure efficiency and safety. Oil field operations often use SCADA or wireless sensor network (WSN)-based monitoring and control systems; both have numerous drawbacks. WSN-based systems are not uniform or are incompatible. Additionally, they lack transparent communication and coordination. SCADA systems also cost a lot, are rigid, are not scalable, and deliver data slowly. Edge computing and the Industrial Internet of Things (IIoT) help to overcome SCADA’s constraints by establishing an automated monitoring and control system for oil and gas operations that is effective, secure, affordable, and transparent. The main objective of this study is to exploit the IIOT and Edge Computing (EC). This study introduces an I²OT-EC framework with flowcharts, a simulator, and system architecture. The validity of the I²OT-EC framework is demonstrated by experimental findings and implementation with an application example to verify the research results as an additional verification and testing that proves the framework results were satisfactory. The significant increase of 12.14% in the runtime for the crude well using the proposed framework, coupled with other advantages, such as reduced operational costs, decentralization, and a dependable platform, highlights the benefits of this solution and its suitability for the automatic monitoring and control of oil field operations. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence and Machine Learning Applied to Energy Efficiency in Building Design)

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23 pages, 4504 KiB

Open AccessArticle

An Improved Optimally Designed Fuzzy Logic-Based MPPT Method for Maximizing Energy Extraction of PEMFC in Green Buildings

by Mokhtar Aly, Emad A. Mohamed, Hegazy Rezk, Ahmed M. Nassef, Mostafa A. Elhosseini and Ahmed Shawky

Energies 2023, 16(3), 1197; https://doi.org/10.3390/en16031197 - 21 Jan 2023

Cited by 4 | Viewed by 1360

Abstract

Recently, the concept of green building has become popular, and various renewable energy systems have been integrated into green buildings. In particular, the application range of fuel cells (FCs) has become widespread due to the various government plans regarding green hydrogen energy systems. In particular, proton exchange membrane fuel cells (PEMFCs) have proven superiority over other existing FCs. However, the uniqueness of the operating maximum power point (MPP) of PEMFCs represents a critical issue for the PEMFC control systems. The perturb and observe, incremental conductance/resistance, and fuzzy logic control (FLC) represent the most used MPP tracking (MPPT) algorithms for PEMFC systems, among which the FLC-based MPPT methods have shown improved performance compared to the other methods. Therefore, this paper presents a modified FLC-based MPPT method for PEMFC systems in green building applications. The proposed method employs the rate of change of the power with current (

d P / d I

) instead of the previously used rate of change of power with voltage (

d P / d V

) in the literature. The employment of

d P / d I

in the proposed method enables the fast-tracking of the operating MPP with low transient oscillations and mitigated steady-state fluctuations. Additionally, the design process of the proposed controller is optimized using the enhanced version of the success-history-based adaptive differential evolution (SHADE) algorithm with linear population size reduction, known as the LSHADE algorithm. The design optimization of the proposed method is advantageous for increasing the adaptiveness, robustness, and tracking of the MPP in all the operating scenarios. Moreover, the proposed MPPT controller can be generalized to other renewable energy and/or FCs applications. The proposed method is implemented using C-code with the PEMFC model and tested in various operating cases. The obtained results show the superiority and effectiveness of the proposed controller compared to the classical proportional-integral (PI) based

d P / d I

-based MPPT controller and the classical FLC-based MPPT controller. Moreover, the proposed controller achieves reduced output waveforms ripple, fast and accurate MPPT operation, and simple and low-cost implementation. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence and Machine Learning Applied to Energy Efficiency in Building Design)

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20 pages, 3692 KiB

Open AccessArticle

Teaching–Learning–Based Optimization (TLBO) in Hybridized with Fuzzy Inference System Estimating Heating Loads

by Loke Kok Foong and Binh Nguyen Le

Energies 2022, 15(21), 8289; https://doi.org/10.3390/en15218289 - 06 Nov 2022

Cited by 1 | Viewed by 1321

Abstract

Nowadays, since large amounts of energy are consumed for a variety of applications, more and more emphasis is placed on the conservation of energy. Recent investigations have experienced the significant advantages of using metaheuristic algorithms. Given the importance of the thermal loads’ analysis in energy-efficiency buildings, a new optimizer method, i.e., the teaching–learning based optimization (TLBO) approach, has been developed and compared with alternative techniques in the present paper to predict the heating loads (HLs). This model is applied to the adaptive neuro–fuzzy interface system (ANFIS) in order to overcome its computational deficiencies. A literature-based dataset acquired for residential buildings is used to feed these models. According to the results, all the applied models can appropriately predict and analyze the heating load pattern. Based on the value of R² calculated for both testing and training (0.98933, 0.98931), teaching–learning-based optimization can help the adaptive neuro–fuzzy interface system to enhance the results’ correlation. Also, the high R² value means that the model has high accuracy in the HL prediction. In addition, according to the estimated RMSE, the training error of TLBO–ANFIS in the testing and training stages was 0.07794 and 0.07984, respectively. The low value of root–mean–square error (RMSE) indicates that the TLBO–ANFIS method acts favorably in the estimation of the heating load for residential buildings. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence and Machine Learning Applied to Energy Efficiency in Building Design)

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22 pages, 4504 KiB

Open AccessArticle

A Honey Badger Optimization for Minimizing the Pollutant Environmental Emissions-Based Economic Dispatch Model Integrating Combined Heat and Power Units

by Ragab El-Sehiemy, Abdullah Shaheen, Ahmed Ginidi and Mostafa Elhosseini

Energies 2022, 15(20), 7603; https://doi.org/10.3390/en15207603 - 14 Oct 2022

Cited by 19 | Viewed by 1968

Abstract

Traditionally, the Economic Dispatch Model (EDM) integrating Combined Heat and Power (CHP) units aims to reduce fuel costs by managing power-only, CHP, and heat-only units. Today, reducing pollutant emissions to the environment is of paramount concern. This research presents a novel honey badger optimization algorithm (HBOA) for EDM-integrated CHP units. HBOA is a novel meta-heuristic search strategy inspired by the honey badger’s sophisticated hunting behavior. In HBOA, the dynamic searching activity of the honey badger, which includes digging and honing, is separated into exploration and exploitation phases. In addition, several modern meta-heuristic optimization algorithms are employed, which are the African Vultures Algorithm (AVO), Dwarf Mongoose Optimization Algorithm (DMOA), Coot Optimization Algorithm (COA), and Beluga Whale Optimization Algorithm (BWOA). These algorithms are applied in a comparative manner considering the seven-unit test system. Various loading levels are considered with different power and heat loading. Four cases are investigated for each loading level, which differ based on the objective task and the consideration of power losses. Moreover, considering the pollutant emissions minimization objective, the proposed HBOA achieves reductions, without loss considerations, of 75.32%, 26.053%, and 87.233% for the three loading levels, respectively, compared to the initial case. Moreover, considering minimizing pollutant emissions, the suggested HBOA achieves decreases of 75.32%, 26.053%, and 87.233%, relative to the baseline scenario, for the three loading levels, respectively. Similarly, it performs reductions of 73.841%, 26.155%, and 92.595%, respectively, for the three loading levels compared to the baseline situation when power losses are considered. Consequently, the recommended HBOA surpasses the AVO, DMOA, COA, and BWOA when the purpose is to minimize fuel expenditures. In addition, the proposed HBOA significantly reduces pollutant emissions compared to the baseline scenario. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence and Machine Learning Applied to Energy Efficiency in Building Design)

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17 pages, 3043 KiB

Open AccessArticle

The Applicability of Biogeography-Based Optimization and Earthworm Optimization Algorithm Hybridized with ANFIS as Reliable Solutions in Estimation of Cooling Load in Buildings

by Hossein Moayedi and Bao Le Van

Energies 2022, 15(19), 7323; https://doi.org/10.3390/en15197323 - 05 Oct 2022

Cited by 5 | Viewed by 1313

Abstract

The foundation of energy-efficient architectural design is modeling heating and cooling loads (HLs and CLs), which defines the heating and cooling apparatus constraints necessary to maintain a suitable interior air environment. It is possible that analytical models for energy-efficient buildings might offer an accurate evaluation of the influence that various building designs would have. The implementation of these instruments, however, might be a process that requires a significant amount of manual labor, a significant amount of time, and is reliant on user experiences. In light of this, the authors of this paper present two unique methods for estimating the CL of residential structures in the form of complex mathematical concepts. These methodologies include an evolutionary web algorithm (EWA), biogeography-based optimization (BBO), and a hybridization of an adaptive neuro-fuzzy interface system (ANFIS), namely BBO-ANFIS and EWA-ANFIS. The findings initiated from each of the suggested models are evaluated with the help of various performance metrics. Moreover, it is possible to determine which model is the most effective by comparing their coefficient of determination (

R^{2}

) and its root mean square error (RMSE) to each other. In mapping non-linear connections between input and output variables, the observed findings showed that the models used have a great capability. In addition, the results showed that BBO-ANFIS was the superior forecasting model out of the two provided models, with the lowest value of RMSE and the greatest value of

R^{2}

(RMSE = 0.10731 and 0.11282 and

R^{2}

= 0.97776 and 0.97552 for training and testing phases, respectively). The EWA-ANFIS also demonstrated RMSE and

R^{2}

values of 0.18682 and 0.17681 and 0.93096 and 0.93874 for the training and testing phases, respectively. Finally, this study has proven that ANN is a powerful tool and will be useful for predicting the CL in residential buildings. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence and Machine Learning Applied to Energy Efficiency in Building Design)

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