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All Journal JURNAL NASIONAL TEKNIK ELEKTRO
Elvis Twumasi
Kwame Nkrumah University of Science and Technology
Electrical & Electronics Engineering

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Journal : JURNAL NASIONAL TEKNIK ELEKTRO

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Potential for Energy Savings in Educational Institutions in Ghana Elvis Twumasi; Emmanuel Asuming Frimpong; Leslie Novihoho
JURNAL NASIONAL TEKNIK ELEKTRO Vol 8, No 3: November 2019
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (410.749 KB) | DOI: 10.25077/jnte.v8n3.661.2019

Abstract

This paper presents the results of an energy audit carried out to assess the potential of energy savings in educational institutions in Ghana using the Kwame Nkrumah University of Science and Technology (KNUST) as the case study institution. It also outlines a simple and effective technique for such an audit. The College of Engineering; one of the six Colleges of KNUST was used as the study location. Light bulbs and fans at the classrooms, corridors, laboratories and washrooms were monitored for energy wastage. The monitoring period was one month. The energy wastage over the period was estimated to be 1718.24kWh, which is high. The yearly energy wastage at KNUST for the areas assessed is projected to be 95.276MWh, which is alarming. Urgent steps are therefore needed to curb this wastage.Keywords: Energy auditing, Energy efficiency, Energy efficiency measures, Energy saving and Energy wastage
Performance Enhancement of Elephant Herding Optimization Algorithm Using Modified Update Operators Abdul-Fatawu Seini Yussif; Elvis Twumasi; Emmanuel Asuming Frimpong
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 2: July 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v12n2.1124.2023

Abstract

This research paper presents a modified version of the Elephant Herding Optimization (EHO) algorithm, referred to as the Modified Elephant Herding Optimization (MEHO) algorithm, to enhance its global performance. The focus of this study lies in improving the balance between exploration and exploitation within the algorithm through the modification of two key operators: the matriarch updating operator and the separation updating operator. By reframing the equations governing these operators, the proposed modifications aim to enhance the algorithm’s ability to discover optimal global solutions. The MEHO algorithm is implemented in the MATLAB environment, utilizing MATLAB R2019a. To assess its efficacy, the algorithm is subjected to rigorous testing on various standard benchmark functions. Comparative evaluations are conducted against the original EHO algorithm, as well as other established optimization algorithms, namely the Improved Elephant Herding Optimization (IEHO) algorithm, Particle Swarm Optimization (PSO) algorithm, and Biogeography-Based Optimization (BBO) algorithm. The evaluation metrics primarily focus on the algorithms’ capacity to produce the best global solution for the tested functions. The proposed MEHO algorithm outperformed the other algorithms on 75% of the tested functions, and 62.5% under two specific test scenarios. The findings highlight the effectiveness of the proposed modification in enhancing the global performance of the Elephant Herding Optimization algorithm. Overall, this work contributes to the field of optimization algorithms by presenting a refined version of the EHO algorithm that exhibits improved global search capabilities.
Co-Authors Abdul-Fatawu Seini Yussif Emmanuel Asuming Frimpong Leslie Novihoho