Modeling and Simulation of Solar Irradiance Conversion Using the Pyranometer App
Authors
Jose C. Agoylo Jr.
BSIT, Southern Leyte State University – Tomas Oppus Campus, Southern Leyte, Philippines (PH)
Alex C. Bacalla
BSIT, Southern Leyte State University – Tomas Oppus Campus, Southern Leyte, Philippines (PH)
Sylvia T. Agoylo
BSIT, Southern Leyte State University – Tomas Oppus Campus, Southern Leyte, Philippines (PH)
Article Information
DOI: 10.51583/ijltemas.2024.130503
Subject Category: Solar irradiance simulation
Volume/Issue: 13/5 | Page No: 17-27
Publication Timeline
Submitted: 2024-06-04
Published: 2024-06-04
Abstract
Solar irradiance is the amount of light energy that the sun emits and reaches the earth. The Pyranometer app is used to gather sample data. This predictive model calculated the electricity a solar module generates based on solar irradiance. It can calculate the voltage, current, and power a solar module can produce from solar irradiance. The study used a developmental-evaluative design using simulation modeling created using Matlab® and Simulink®. The evaluation of the model's accuracy was confirmed on a parallel run with an existing renewable solar energy facility to assess functionality, performance, robustness, workmanship, and the overall recommendation of the model. Findings revealed promising results that the designed model could, with reliable accuracy, simulate a solar module's performance from a given amount of irradiance.
Keywords
solar module, irradiance, pyranometer, predictive model, simulation modeling
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References
1. Abedinia, O., Amjady, N., & Ghadimi, N. (2018). Solar energy forecasting based on a hybrid neural network and improved metaheuristic algorithm. Computational Intelligence, 34(1), 241-260. [Google Scholar] [Crossref]
2. Altiok, T., & Melamed, B. (2010). Simulation modeling and analysis with Arena. Elsevier. [Google Scholar] [Crossref]
3. Anagha, M. J., Rao, S. N., & Bennaceur, K. (2018, December). A Survey of Solar Irradiance Measurement Techniques. (pp. 1–4). IEEE. [Google Scholar] [Crossref]
4. Antonanzas-Torres, F., Urraca, R., Antonanzas, J., Fernandez-Ceniceros, J., & Martinez-de-Pison, F. J. (2015). Generation of daily global solar irradiation with support vector machines for regression. Energy conversion and management, 96, 277-286. [Google Scholar] [Crossref]
5. EERE. (2013, August 21). Solar Radiation Basics. Retrieved April 18, 2019, from https://www.energy.gov/ [Google Scholar] [Crossref]
6. Gow, J. A., & Manning, C. D. (1999). Development of a photovoltaic array model for use in power-electronics simulation studies. IEE Proceedings - Electric Power Applications, 146(2), 193. doi:10.1049/ip-epa:19990116 [Google Scholar] [Crossref]
7. Hukseflux Thermal Sensors B.V. (2011). Pyranometer App Manual. Retrieve from http://www.hukseflux.com [Google Scholar] [Crossref]
8. Kleissl, J. (2013). Solar energy forecasting and resource assessment. Academic Press. [Google Scholar] [Crossref]
9. Nfaoui, M., & El-Hami, K. (2018). Extracting the maximum energy from solar panels. Energy Reports, 4, 536-545. [Google Scholar] [Crossref]
10. Sheng, H., Xiao, J., Cheng, Y., Ni, Q., & Wang, S. (2018). Solar power forecasting for the near future using weighted Gaussian process regression. IEEE Transactions on Industrial Electronics, 65(1), 300-308 [Google Scholar] [Crossref]
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