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All Journal Bulletin of Electrical Engineering and Informatics Journal of Robotics and Control (JRC)
Hasan, Raed Abdulkareem
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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Unlocking Solar Potential: Advancements in Automated Solar Tracking Systems for Enhanced Energy Utilization Hussain, Abadal-Salam T.; Hakim, Baqer A; Ahmed, Saadaldeen Rashid; Abed, Tareq Hamad; Taha, Taha A.; Hasan, Taif. S.; Hasan, Raed Abdulkareem; Hashim, Abdulghafor Mohammed; Tawfeq, Jamal Fadhil
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.19931

Abstract

The use of solar tracking systems has become vital and has established itself as a vital element in the generation of solar energy by enhancing the collection efficiency. This paper seeks to understand the necessity of shifting from conventional energy sources and why issues like scarcity of fossil fuel, and pollution are some of the hurdles toward achieving sustainable energy. Solar power, in particular, is one of the lights at the end of this tunnel since it pioneers a shift towards the usage of clean energy in the world. The subject of interests of the study is on how tracking systems help in maximizing energy collection from solar systems by interchanging it with the movement of sun’s path. It discusses the method that was followed, which involves selecting component, designing circuit and developing software together with presenting empirical data that was obtained from a three-day, Twenty-four-hour experiment. Outcomes show that there is an improvement on voltage stability, the level of solar irradiation and temperature regulation when the system is applied as compared to static system and its applicability for the enhancement of the renewable energy harnessing methods by using the solar tracking technology. Finally, it outlines the future research directions to continue exploring the proposed methods and its wider impact on renewable energy generation.
The effectiveness of big data classification control based on principal component analysis Mohammed, Mostafa Abdulghafoor; Akawee, Mostafa Mahmood; Saleh, Ziyad Hussien; Hasan, Raed Abdulkareem; Ali, Ahmed Hussein; Sutikno, Tole
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4405

Abstract

Large-scale datasets are becoming more common, yet they can be challenging to understand and interpret. When dealing with big datasets, principal component analysis (PCA) is used to minimize the dimensionality of the data while maintaining interpretability and avoiding information loss. It accomplishes this by producing new uncorrelated variables that gradually reduce the variance of the system. In the field of data analysis, PCA is a multivariate statistical technique commonly used to obtain rules explaining the separation of groups in a given situation. Classes are predicted using a classification algorithm, a supervised learning technique that indicates which type of data points will be presented. Creating a classification model using classification algorithms is required before any successful classification can be achieved. It is possible to predict the future using a variety of categorized strategies. It is necessary to reduce the dimensionality of data sets using the PCA approach. This article will begin by introducing the basic ideas of PCA and discussing what it can and cannot do. It will then describe some variants of PCA and their application and then shows how PCA improves the performance using a series of experiments.
Co-Authors Abed, Tareq Hamad Ahmed, Saadaldeen Rashid Akawee, Mostafa Mahmood Ali, Ahmed Hussein Hakim, Baqer A Hasan, Taif. S. Hashim, Abdulghafor Mohammed Hussain, Abadal-Salam T. Mohammed, Mostafa Abdulghafoor Saleh, Ziyad Hussien Taha, Taha A. Tawfeq, Jamal Fadhil Tole Sutikno