Abstract
Discovering factors and relationships that affect the temperature of photovoltaic module panel is vital to solar energy based research in an attempt to generate green energy effectively. Despite the availability of visualization tools that assist in the discovery, they are not dedicated. They require some efforts, time and certain level of skills to gain benefits out of them. Thus, this research has designed and developed a dedicated application system called RAID Explorer to assist the researchers in their attempts to explore various factors that contribute to photovoltaic module temperature. With the system, discoveries are through observation that stimulates human cognitive ability. The system strength’s resides in its three main components, which are data entry, visualization and visual analytics. The components are flexible as such, visual interaction is intuitive and users are able to manage it with minimum instruction. They also enable personal customization as such judicious judgment can be made. For performance analysis, the system was implemented and utilized by Green Energy Research Centre (GERC), Universiti Teknologi MARA (UiTM), Malaysia to determine its functionality and usability. The findings show that RAID capabilities are beyond merely to discover the factors contributed to photovoltaic module temperature. In fact, it has successfully supported the discovering of various relationships in the photovoltaic dataset. Thus, RAID Explorer supports scientists’ need for flexible, speed and interactive exploration of huge photovoltaic dataset in relation to relationship discovery.
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Acknowledgments
The authors would like to thank Universiti Teknologi MARA, Malaysia and Ministry of Higher Education Malaysia for the facilities and financial support under the national grant 600-IRMI/FRGS 5/3 (022/2017), FRGS/1/2017/1CT04/UITM/02/2.
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Idrus, Z., Abdullah, N.A.S., Zainuddin, H., Ja’afar, A.D.M. (2017). Software Application for Analyzing Photovoltaic Module Panel Temperature in Relation to Climate Factors. In: Mohamed, A., Berry, M., Yap, B. (eds) Soft Computing in Data Science. SCDS 2017. Communications in Computer and Information Science, vol 788. Springer, Singapore. https://doi.org/10.1007/978-981-10-7242-0_17
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