| Autor: |
Seshadri, Parthasarathy, T.S., Bagavat Perumaal, B., Ashok Kumar, H., Keerthana, G., Kavinmathi, S., Senthilrani |
| Předmět: |
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| Zdroj: |
Electric Power Components & Systems; 2021, Vol. 49 Issue 16/17, p1379-1388, 10p |
| Abstrakt: |
The increase in penetration of solar photovoltaics into the traditional grid and the accelerating growth of smart grids have introduced new challenges to grid stability. Forecasting the output power from solar PV systems and time-based analysis for the performance characteristics of solar PV under different weather conditions is essential to improve the grid stability. The generated PV power is intermittent in nature and is influenced by meteorological parameters such as pressure, temperature, relative humidity, and solar zenith angle. With the influence of the above parameters, a novel power forecasting model has been developed using Supervised Machine Learning Algorithm. The historical weather data of a given location have been fetched from National Solar Radiation Database (NSRDB) with the corresponding location coordinates. Multivariate data are used as inputs to train a Decision Tree Regression Model in order to predict the solar irradiance parameters such as Global Horizontal Irradiance, Direct Normal Irradiance, and Diffuse Horizontal Irradiance which are essential to calculate the output power harnessed from the grid-connected PV system. The results are favorable for the application and have depicted minimal deviation with an average accuracy of 86.02%. This technique also rules out the need of hardware power prediction modules, favoring a cost-efficient methodology. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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