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Special Issue "Sophisticated Soft Computing Techniques for Sustainable Engineering and Sciences"

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Special Issue Editors

Special Issue Information

Dear Colleagues,

Presently, sustainability engineering and science is a rapidly developing research area that seeks to comprehend the fundamental characteristics of the connection between society and nature. However, it faces intricate challenges due to its high degree of complexity; therefore, it is essential to introduce sophisticated approaches, tools, and techniques that facilitate stakeholders and decision makers to make decisions by considering the broad range of uncertainty with diminutive data sets. Sophisticated soft computing techniques have the potential to solve complex problems in various applications, irrespective of the domain. The main objective of this Special Issue is to consolidate the most advanced soft computational approaches to solve cumbersome problems experienced in various applications that also lead to sustainable development in engineering and sciences.

We are seeking sophisticated soft computing approaches based on well-defined work that considers the topics mentioned below but that is not necessarily limited to the following areas:

Sustainable natural resources and environmental modelling;
Sustainable waste management;
Air quality modeling;
Crop yield prediction and drought prediction;
Green and 5G/6G communication systems;
Renewable energy resources for sustainable power generation;
Sustainable transportation;
Smart metering for sustainable power distribution;
Smart cities;
Green buildings;
Interaction of machine learning with IoT;
Forecasting problems;
Disaster management;
Health care applications.

We hope this Special Issue will reach precise, concrete, and concise conclusions that will make significant contributions to the establishment of new horizons for future research directions.

Dr. Mohammed H. Alsharif
Dr. Kannadasan Raju
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. Sustainability 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 2400 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

soft computing
internet of things
communication systems
renewable energy resources
agronomy
smart cities
health care engineering
disaster management
smart transportation

Published Papers (2 papers)

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Research

Open AccessArticle

Prediction of Cooling Load of Tropical Buildings with Machine Learning

Gebrail Bekdaş

Yaren Aydın

Ümit Isıkdağ

Aidin Nobahar Sadeghifam

Sanghun Kim

and

Zong Woo Geem

Sustainability 2023, 15(11), 9061; https://doi.org/10.3390/su15119061 - 03 Jun 2023

Cited by 2 | Viewed by 588

Abstract

Cooling load refers to the amount of energy to be removed from a space (or consumed) to bring that space to an acceptable temperature or to maintain the temperature of a space at an acceptable range. The study aimed to develop a series of models and determine the most accurate ones in the prediction of the cooling load of low-rise tropical buildings based on their basic architectural and structural characteristics. In this context, a series of machine learning (regression) algorithms were tested during the research to determine the most accurate/efficient prediction model. In this regard, a data set consisting of ten features indicating the basic characteristics of the building (floor area, aspect ratio, ceiling height, window material, external wall material, roof material, window wall ratio north faced, window wall ratio south faced, horizontal shading, orientation) were used to predict the cooling load of a low-rise tropical building. The dataset was generated utilizing a set of generative and algorithmic design tools. Following the dataset generation, a series of regression models were tested to find the most accurate model to predict the cooling load. The results of the tests with different algorithms revealed that the relationship between the predictor variables and cooling load could be efficiently modeled through Histogram Gradient Boosting and Stacking models. Full article

(This article belongs to the Special Issue Sophisticated Soft Computing Techniques for Sustainable Engineering and Sciences)

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Open AccessArticle

A Novel Hybrid MPPT Approach for Solar PV Systems Using Particle-Swarm-Optimization-Trained Machine Learning and Flying Squirrel Search Optimization

Dilip Kumar

Yogesh Kumar Chauhan

Ajay Shekhar Pandey

Ankit Kumar Srivastava

Varun Kumar

Faisal Alsaif

Rajvikram Madurai Elavarasan

Md Rabiul Islam

Raju Kannadasan

and

Mohammed H. Alsharif

Sustainability 2023, 15(6), 5575; https://doi.org/10.3390/su15065575 - 22 Mar 2023

Cited by 2 | Viewed by 1600

Abstract

In this paper, a novel hybrid Maximum Power Point Tracking (MPPT) algorithm using Particle-Swarm-Optimization-trained machine learning and Flying Squirrel Search Optimization (PSO_ML-FSSO) has been proposed to obtain the optimal efficiency for solar PV systems. The proposed algorithm was compared with other well-known methods viz. Perturb & Observer (P&O), Incremental Conductance (INC), Particle Swarm Optimization (PSO), Cuckoo Search Optimization (CSO), Flower Pollen Algorithm (FPA), Gray Wolf Optimization (GWO), Neural-Network-trained Machine Learning (NN_ML), Genetic Algorithm (GA), and PSO-trained Machine Learning. The proposed algorithm was modelled in the MATLAB/Simulink environment under different operating conditions, for example, with step changes in temperature, solar irradiance, and partial shading. The proposed algorithm improved the efficiency up to 0.72% and reduced the settling time up to 76.4%. The findings of the research highlight that PSO_ML-FSSO is a potential approach that outperforms all other well-known algorithms tested herein for solar PV systems. Full article

(This article belongs to the Special Issue Sophisticated Soft Computing Techniques for Sustainable Engineering and Sciences)

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