Understanding Solar Power: Analyzing and Predicting Photovoltaic Energy Output
DOI:
https://doi.org/10.55524/ijirem.2023.10.4.7Keywords:
Solar Energy Prediction, MAPEAbstract
This paper compares data-driven algorithms (Linear Regression, Random Forest, and Decision Tree) for solar energy prediction. It analyzes variables like Daily Yield, Total Yield, Ambient Temperature, Module Temperature, Irradiation, and DC Power using a dataset with unprecedented granularity. The algorithms were trained and tuned for optimal performance, resulting in high accuracy levels. Linear Regression achieved 99.4% accuracy, Random Forest achieved 99.2% accuracy, and Decision Tree had the highest accuracy at 99.8%. The analysis identified strengths and weaknesses of each algorithm, indicating their suitability for different prediction scenarios. These findings have significant implications for integrating solar energy into the power system, instilling confidence in the reliability of data-driven algorithms for precise solar energy forecasting.
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