ORCID Profile
0000-0003-0307-0912
Current Organisation
The University of Newcastle
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Publisher: LPPM Unsyiah
Date: 15-08-2023
Publisher: IEEE
Date: 12-2017
Publisher: Wiley
Date: 24-02-2020
Publisher: MDPI AG
Date: 09-08-2022
DOI: 10.3390/SU14169846
Abstract: This study is about the electrification of the remote islands in the Indian Ocean that were severely affected by the tsunami in the 2004 earth earthquake. To supply electricity to the islands, two diesel generators with capacities of 110 kW and 60 kW were installed in 2019. The feasibility of using renewable energy to supplement or replace the units in these two generators is investigated in this work. In 2019, two diesel generators with capacities of 110 kW and 60 kW were installed in the islands to supply electricity. This work analyses whether the viability of using renewable energy can be used to supplement or replace these two generators. Among the renewable energy options proposed here are a 100 kW wind turbine, solar PV, a converter, and batteries. As a result, the study’s goal is to perform a techno-economic analysis and optimise the proposed hybrid diesel and renewable energy system for a remote island in the Indian Ocean. The Hybrid Optimisation Model for Electric Renewable (HOMER) Pro software was used for all simulations and optimisation for this analysis. The calculation is based on the current diesel price of USD 0.90 per litre (without subsidy). The study found that renewable alone can contribute to 29.2% of renewable energy fractions based on the most optimised systems. The Net Present Cost (NPC) decreased from USD 1.65 million to USD 1.39 million, and the levelised Cost of Energy (CoE) decreased from 0.292 USD/kWh to 0.246 USD/kWh, respectively. The optimised system’s Internal Rate of Return (IRR) is 14% and Return on Investment (ROI) 10%, with a simple payback period of 6.7 years. This study shows that it would be technically feasible to introduce renewable energy on a remote island in Indonesia, where numerous islands have no access to electricity.
Publisher: Graduate Program of Management and Business, Bogor Agricultural University
Date: 22-05-2023
Publisher: Wiley
Date: 05-08-2019
Publisher: IEEE
Date: 23-11-2022
Publisher: MDPI AG
Date: 24-06-2022
DOI: 10.3390/SU14137735
Abstract: This study aimed to conduct a techno-economic performance and optimisation analysis of grid-connected PV, wind turbines, and battery packs for Syiah Kuala University, situated at the tip of Sumatra island in the tsunami-affected region. The simulation software Hybrid Optimisation Model for Electric Renewables (HOMER) was used to analyse and optimise the renewable energy required by the institution. The methodology began with the location specification, average electric load demand, daily radiation, clearness index, location daily temperature, and system architecture. The results revealed that the energy storage system was initially included in the simulation, but it was later removed in order to save money and optimise the share of renewable energy. Based on the optimisation results, two types of energy sources were chosen for the system, solar PV and wind turbine, which contributed 62% and 20%, respectively. Apart from the renewable energy faction, another reason for the system selection is cost of energy (CoE), which decreased to $0.0446/kWh from $0.060/kWh. In conclusion, the study found that by connecting solar PV and wind turbines to the local grid, this renewable energy system is able to contribute up to 82% of the electricity required. However, the obstacle to implementing renewable energy in Indonesia is the cheap electricity price that is mainly generated using cheap coal, which is abundantly available in the country.
Publisher: Springer International Publishing
Date: 2018
Publisher: IEEE
Date: 11-2016
Publisher: Wiley
Date: 13-05-2020
Publisher: Universitas Airlangga
Date: 30-01-2023
DOI: 10.20473/JR.V9-I.1.2023.30-36
Abstract: Introduction: Every area of our lives has been devastated by the worldwide Coronavirus disease 2019 (COVID-19) epidemic. However, the development of artificial intelligence has made it possible to build advanced applications that can fulfill this level of clinical accuracy. This study aimed to create a deep learning model that can detect COVID-19 from a chest image dataset of confirmed patients treated at the provincial hospital in Aceh. Methods: Eight hundred confirmed COVID-19 patients' chest X-ray photos were gathered locally from Dr. Zainoel Abidin General Hospital, Banda Aceh. Performance was evaluated in several ways. First, the dataset was used for training and testing. Second, the data was used to train and test the model. VGG16 is a robust network adapted to an enhanced dataset constructed from a confirmed COVID-19 chest X-ray pool. To artificially produce a huge number of chest X-ray pictures, this study used data augmentation techniques such as random rotation at an angle between 10 and 10°, random noise, and horizontal flips. Results: The experimental results were encouraging: the proposed models classified chest X-ray pictures as normal or COVID-19 with an accuracy of 97.20% for Resnet50, 98.10% for InceptionV3, and 98.30% for VGG16. The results showed the outstanding performance of straightforward COVID-19 diagnosis with the classification of COVID-19 severity, such as mild, severe, and very severe. Conclusion: These made it possible to automate the X-ray image interpretation process accurately and could also be applied when materials and reverse transcription polymerase chain reaction (RT-PCR) tests are scarce.
Location: United States of America
Location: No location found
No related grants have been discovered for Teuku Geumpana.