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Ganapathy Ramesh, Jaganathan Logeshwaran, Thangavel Kiruthiga and Jaime Lloret
In general, reliable PV generation prediction is required to increase complete control quality and avoid potential damage. Accurate forecasting of direct solar radiation trends in PV power production could limit the influence of uncertainties on photovol...
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Muhammad Waqas Zaffar, Ishtiaq Haasan and Abdul Razzaq Ghumman
The present study investigated the performance of three different stilling basins, i.e., modified United State Bureau of Reclamation (USBR) Type III, USBR Type II, and wedge-shaped baffle blocks (WSBB), using FLOW-3D scour models. Field data of the river...
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Sanguk Park
This study aims to enable cost-effective Internet of Things (IoT) system design by removing redundant IoT sensors through the correlation analysis of sensing data collected in a smart home environment. This study also presents a data analysis and predict...
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Guwon Yoon, Seunghwan Kim, Haneul Shin, Keonhee Cho, Hyeonwoo Jang, Tacklim Lee, Myeong-in Choi, Byeongkwan Kang, Sangmin Park, Sanghoon Lee, Junhyun Park, Hyeyoon Jung, Doron Shmilovitz and Sehyun Park
Energy prediction models and platforms are being developed to achieve carbon-neutral ESG, transition buildings to renewable energy, and supply sustainable energy to EV charging infrastructure. Despite numerous studies on machine learning (ML)-based predi...
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Lingbo Nan, Yumeng Wang, Diyi Chen, Weining Huang, Zuchao Zhu and Fusheng Liu
Traditional centrifugal pump performance prediction (CPPP) employs the semi-theoretical and semi-empirical approaches; however, it can lead to many prediction errors. Considering the superiority of deep learning when applied to nonlinear systems, in this...
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