Nature-Inspired Science and Engineering for Sustainable Future

A special issue of Biomimetics (ISSN 2313-7673). This special issue belongs to the section "Energy Biomimetics".

Deadline for manuscript submissions: 20 February 2025 | Viewed by 1077

Special Issue Editor


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Guest Editor
1. ABSCUBE Engineering & Education Services Pty Ltd., Melbourne, VIC, Australia
2. School of Engineering, RMIT University, Melbourne, VIC 3000, Australia
Interests: aerodynamics; bioengineering; turbo machinery; mechanical design; energy and power; renewable energy; engineering education and accreditation
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Special Issue Information

Dear Colleagues,

In the quest for sustainable and renewable energy sources, scientists and engineers have increasingly turned to nature for inspiration. Biomimicry learns from and mimics the strategies found in nature and has become an effective tool for creating ground-breaking renewable energy solutions. Several new technologies have been inspired by nature as designers increasingly look to biomimicry for creating new ideas for wind turbines, solar cells, and hydropower. For instance, scientists studied the effective distribution of water and nutrients by leaf veins, which provided inspiration for the microchannel design of solar panels. Scientists also studied the structure and movement of bird wings, using bionic concepts to develop flexible wing structures and streamlined shapes.

This Special Issue will investigate biomimetics as drivers of design on several scales as a tool for sustainable development, which focused on bio-inspired approaches used for reducing the operational energy of different engineering systems including that of buildings, road vehicles, passenger cars, trucks, locomotives, aeroplanes/aircrafts, submarines, or underwater vehicles. Scientific contributions are invited from scientists, researchers, engineers, and industry professionals as a means of disseminating recent design strategies, inventions, and developments in the field. Review papers presenting the state of the art of this research area and identifying new directions for further research are also welcome.

This Special Issue was developed in collaboration with the Australian Society of Energy and Power (ASEP) and aligned with the 5th International Conference on Energy and Power (ICEP2024), and for the Conference details, see the following link: https://www.asep.org.au/icep-conference/5th-icep-2024.

Dr. Harun Chowdhury
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. Biomimetics is an international peer-reviewed open access monthly 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 2200 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

  • biomimicry design
  • power and energy technologies
  • bio-energy
  • renewable
  • efficiency
  • sustainability

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Published Papers (1 paper)

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Research

25 pages, 1355 KiB  
Article
Performance Comparison of Bio-Inspired Algorithms for Optimizing an ANN-Based MPPT Forecast for PV Systems
by Rafael Rojas-Galván, José R. García-Martínez, Edson E. Cruz-Miguel, José M. Álvarez-Alvarado and Juvenal Rodríguez-Resendiz
Biomimetics 2024, 9(10), 649; https://doi.org/10.3390/biomimetics9100649 - 21 Oct 2024
Viewed by 847
Abstract
This study compares bio-inspired optimization algorithms for enhancing an ANN-based Maximum Power Point Tracking (MPPT) forecast system under partial shading conditions in photovoltaic systems. Four algorithms—grey wolf optimizer (GWO), particle swarm optimization (PSO), squirrel search algorithm (SSA), and cuckoo search (CS)—were evaluated, with [...] Read more.
This study compares bio-inspired optimization algorithms for enhancing an ANN-based Maximum Power Point Tracking (MPPT) forecast system under partial shading conditions in photovoltaic systems. Four algorithms—grey wolf optimizer (GWO), particle swarm optimization (PSO), squirrel search algorithm (SSA), and cuckoo search (CS)—were evaluated, with the dataset augmented by perturbations to simulate shading. The standard ANN performed poorly, with 64 neurons in Layer 1 and 32 in Layer 2 (MSE of 159.9437, MAE of 8.0781). Among the optimized approaches, GWO, with 66 neurons in Layer 1 and 100 in Layer 2, achieved the best prediction accuracy (MSE of 11.9487, MAE of 2.4552) and was computationally efficient (execution time of 1198.99 s). PSO, using 98 neurons in Layer 1 and 100 in Layer 2, minimized MAE (2.1679) but had a slightly longer execution time (1417.80 s). SSA, with the same neuron count as GWO, also performed well (MSE 12.1500, MAE 2.7003) and was the fastest (987.45 s). CS, with 84 neurons in Layer 1 and 74 in Layer 2, was less reliable (MSE 33.7767, MAE 3.8547) and slower (1904.01 s). GWO proved to be the best overall, balancing accuracy and speed. Future real-world applications of this methodology include improving energy efficiency in solar farms under variable weather conditions and optimizing the performance of residential solar panels to reduce energy costs. Further optimization developments could address more complex and larger-scale datasets in real-time, such as integrating renewable energy sources into smart grid systems for better energy distribution. Full article
(This article belongs to the Special Issue Nature-Inspired Science and Engineering for Sustainable Future)
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