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- W4387251572 abstract "Prediction or forecasting the power output of a solar photovoltaic (PV) system is very important for the efficient planning of the energy distribution and utilization. This study uses machine learning algorithms such as linear regression, lasso regression, ridge regression, elastic net regression, support vector regression, and XGBoost regression to forecast the power production of a PV system installed at The NorthCap University, Gurugram (Delhi/NCR). The recorded data of irradiance and ambient temperature was collected from solar PV system over a three-month period which recorded every day at three-hour intervals. The data set includes 733 datapoints in three distinct dimensions: irradiance, ambient temperature, and power output from August to October 2022. The findings demonstrate that XGBoost Regression is the most accurate of the regression models examined, as per the R-squared value achieved between the predicted and actual power output values. All other models also behaved comparable for this data of August to October, as during these months the climate variations and hence irradiance variations are not unusual." @default.
- W4387251572 created "2023-10-03" @default.
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- W4387251572 date "2023-07-29" @default.
- W4387251572 modified "2023-10-06" @default.
- W4387251572 title "Solar Power Prediction in North India Using Different Regression Models" @default.
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- W4387251572 doi "https://doi.org/10.1109/aic57670.2023.10263912" @default.
- W4387251572 hasPublicationYear "2023" @default.
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