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All Journal JURNAL NASIONAL TEKNIK ELEKTRO
Emmanuel Asuming Frimpong
Kwame Nkrumah University of Science and Technology
Electrical & Electronics Engineering

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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
Average Voltage and Multilayer Perceptron Neural Network Based Scheme to Predict Transient Stability Status Emmanuel Asuming Frimpong; Philip Okyere; Johnson Asumadu
JURNAL NASIONAL TEKNIK ELEKTRO Vol 8, No 2: July 2019
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (289.323 KB) | DOI: 10.25077/jnte.v8n2.668.2019

Abstract

This paper presents a technique that predicts the transient stability status of a power system after a disturbance. It uses generator bus voltage as input parameter and a trained single-input multilayer perceptron neural network (MLPNN) as decision tool. When activated, the scheme samples voltages of all generator buses. Two sets of voltage values are extracted from each sampled generator bus voltage. For each set, the minimum voltage value is obtained. An average value is computed from the minimum voltage values extracted from the first sample sets of the various generator buses. The average value is then used to compute the deviations of the minimum voltage values from the second sets of data. The deviations are then summed and used as input to a trained MLPNN which indicates the stability status. The technique was tested using the IEEE 39-bus test system and its accuracy found to be 98.97%.
ANN-Based Electricity Theft Classification Technique for Limited Data Distribution Systems Monister Yaw Kwarteng; Francis Boafo Effah; Daniel Kwegyir; Emmanuel Asuming Frimpong
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 1: March 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v12n1.1072.2023

Abstract

Electricity theft has been a challenge for distribution systems over the years. Theft presents a massive cost to the system operators and other issues such as transformer overloading, line loading, etc. It has become crucial for measures to be implemented to combat illegal electricity consumption. This work sought to develop an artificial neural network-based electricity theft classifier for distribution systems with limited data, i.e., systems that can only provide consumption data alone and no auxiliary data. First, a novel data pre-processing method was proposed for the systems with consumption data only. Again, synthetic minority oversampling is employed to deal with the unbalance problem in the theft detection dataset. Afterwards, an artificial neural network (ANN)-based classifier was proposed to classify customers as normal or fraudulent. The proposed method was tested on actual electricity theft data from the Electricity Company of Ghana (ECG) and its performance compared to random forest (RF) and logistic regression (LR) classifiers. The proposed ANN-based classifier performed exceptionally by producing the best results over RF and LR regarding precision, recall, F1-score, and accuracy of 99.49%, 100%, 99.75%, and 99.74%, respectively.
Short-Term EV Charging Demand Forecast with Feedforward Artificial Neural Network Francis Boafo Effah; Daniel Kwegyir; Daniel Opoku; Peter Asigri; 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.v12n2.1094.2023

Abstract

The global increase in greenhouse gas emissions from automobiles has brought about the manufacture and usage of large quantities of electric vehicles (EVs). However, to ensure proper integration of EVs into the grid, there is a need to forecast the charging demand of EVs accurately. This paper presents a short-term electric vehicle charging demand forecast using a feedforward artificial neural network optimized with a modified local leader phase spider monkey optimization (MLLP-SMO) algorithm, a proposed variant of spider monkey optimization. A proportionate fitness selection is employed to improve the update process of the local leader phase of the spider monkey optimization. The proposed algorithm trains a feedforward neural network to forecast electric vehicle charging demand. The effectiveness of the proposed forecasting model was tested and validated with electric vehicle public charging data from the United Kingdom Power Networks Low Carbon London Project. The model's performance was compared to a feedforward neural network trained with particle swarm optimization, genetic algorithm, classical spider monkey optimization, and two conventional forecasting models, multi-linear regression and Monte Carlo simulation. The performance of the proposed forecasting model was assessed using the mean absolute percentage error of forecast and forecasting accuracy. The model produced a forecast accuracy and mean absolute percentage error of 99.88% and 3.384%, respectively. The results show that MLLP-SMO as a trainer predicted better than the other forecasting models and met industry standard forecast accuracy.
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 Daniel Kwegyir Daniel Opoku Elvis Twumasi Francis Boafo Effah Johnson Asumadu Leslie Novihoho Monister Yaw Kwarteng Peter Asigri Philip Okyere