Solar PV Azimuth Angle Modeling Based on Eight Cardinal Directions Using a Support Vector Machine

The inclination of the azimuth angle greatly affects the output of PV solar power. The precision of the rectangular angle of the sun towards the field of PV modules is something that must be considered when installing PV. Previous researchers have made various calculations using a variety of methods. Determining the correct angle of azimuth requires complex mathematical calculations and knowledge of related points of location. This study presented a different perspective on determining the azimuth angle of PV solar installations. Using a support vector machine algorithm (SVM), machine learning is used for modeling solar PV azimuth angles. The azimuth angle with the eight cardinal directions is tried and combined to produce optimal output. Azimuth angle study data is used as data training and testing for modeling. Modeling results using SVM on radial kernel functions with a value of 1.0 and a tolerance value of 0.0 yield a performance root mean squared error (RMSE) of 0.051. The resulting studies are worthy of application and can help facilitate the design and planning of PV solar installations.