Podrobná bibliografie
| Název: |
PERFORMANCE ANALYSIS OF SOLAR AND WIND ENERGY SYSTEMS USING PYTHON AND NUMERICAL MODELLING. |
| Alternate Title: |
АНАЛІЗ ЕФЕКТИВНОСТІ СОНЯЧНИХ ТА ВІТРОВИХ ЕНЕРГЕТИЧНИХ СИСТЕМ З ВИКОРИСТАННЯМ PYTHON ТА ЧИСЕЛЬНОГО МОДЕЛЮВАННЯ. (Ukrainian) |
| Autoři: |
Viswanatha, Rao J., Dakka, Obulesu, Seeli, Sunanda, Lakshmi, Swarupa Malladi, Olena, Rubanenko |
| Zdroj: |
Renewable Energy / Vidnovluvana Energetyka; 2025, Vol. 82 Issue 3, p137-144, 8p |
| Témata: |
SOLAR energy, WIND power, PYTHON programming language, COMPUTER simulation, SUSTAINABILITY, RENEWABLE energy sources, SENSITIVITY analysis, OPERATIONS research |
| Abstrakt: |
This research conducts a performance evaluation of solar and wind energy systems through numerical modeling using Python. Solar and wind energy rank among the most prevalent renewable energy sources, recognized for their sustainability and minimal environmental footprint. The model developed in this study incorporates actual meteorological data and system specifications to assess the performance of both energy systems under diverse environmental conditions. In the case of solar energy, the model computes power output by taking into account solar irradiance, panel efficiency, and temperature influences. For wind energy, it evaluates power generation by analyzing wind speed, air density, and turbine features. The analysis utilizes Python libraries such as NumPy and Pandas for data processing, while Matplotlib is employed to create comprehensive visual representations of output trends and system dynamics. A sensitivity analysis is performed to pinpoint critical factors affecting performance. The findings indicate that Python-based modeling is a valuable tool for enhancing system efficiency and bolstering the reliability of renewable energy infrastructure. [ABSTRACT FROM AUTHOR] |
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Copyright of Renewable Energy / Vidnovluvana Energetyka is the property of Institute of Renewable Energy of NAS of Ukraine and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Databáze: |
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