Version 1
: Received: 30 December 2023 / Approved: 3 January 2024 / Online: 3 January 2024 (08:36:12 CET)
How to cite:
Lorilla, F. M. A.; Barroca, R. Discrete Level DNI Classification for Enhanced Solar Energy Analysis. Preprints2024, 2024010160. https://doi.org/10.20944/preprints202401.0160.v1
Lorilla, F. M. A.; Barroca, R. Discrete Level DNI Classification for Enhanced Solar Energy Analysis. Preprints 2024, 2024010160. https://doi.org/10.20944/preprints202401.0160.v1
Lorilla, F. M. A.; Barroca, R. Discrete Level DNI Classification for Enhanced Solar Energy Analysis. Preprints2024, 2024010160. https://doi.org/10.20944/preprints202401.0160.v1
APA Style
Lorilla, F. M. A., & Barroca, R. (2024). Discrete Level DNI Classification for Enhanced Solar Energy Analysis. Preprints. https://doi.org/10.20944/preprints202401.0160.v1
Chicago/Turabian Style
Lorilla, F. M. A. and Renyl Barroca. 2024 "Discrete Level DNI Classification for Enhanced Solar Energy Analysis" Preprints. https://doi.org/10.20944/preprints202401.0160.v1
Abstract
This study advances the field of solar irradiance nowcasting by introducing a discrete-level classification approach, diverging from traditional continuous measurement methods. Grounded in a need for cost-effective and modular solutions, the research employs high-resolution computer vision coupled with a deep learning framework to predict Direct Normal Irradiance (DNI). By harnessing the capabilities of a Logitech C900 1080p HD camera and an NVIDIA Jetson module, the study achieves real-time data processing, pivotal for CSP systems' operational efficiency. The core of the methodology is the ResNet-50 convolutional neural network, refined via transfer learning on a bespoke dataset, culminating in a predictive accuracy of 85.78%. This discrete classification model contrasts with conventional, costly instruments like the MS-57, offering a novel and accessible alternative for DNI estimation. Such innovation not only demonstrates high predictive accuracy but also signifies a shift towards less resource-intensive and more adaptable solar energy forecasting tools, contributing a significant leap towards optimizing renewable energy systems.
Keywords
solar irradiance nowcasting; deep learning in renewable energy; modular solar forecasting systems; computer vision for DNI estimation; ResNet-50 for energy prediction; cost-effective solar technology; high-resolution image processing; NVIDIA Jetson in solar energy; classification-based DNI analysis; sustainable energy optimization
Subject
Engineering, Energy and Fuel Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
