  This paper presents a machine learning-based approach for predicting solar
power generation with high accuracy using a 99% AUC (Area Under the Curve)
metric. The approach includes data collection, pre-processing, feature
selection, model selection, training, evaluation, and deployment. High-quality
data from multiple sources, including weather data, solar irradiance data, and
historical solar power generation data, are collected and pre-processed to
remove outliers, handle missing values, and normalize the data. Relevant
features such as temperature, humidity, wind speed, and solar irradiance are
selected for model training. Support Vector Machines (SVM), Random Forest, and
Gradient Boosting are used as machine learning algorithms to produce accurate
predictions. The models are trained on a large dataset of historical solar
power generation data and other relevant features. The performance of the
models is evaluated using AUC and other metrics such as precision, recall, and
F1-score. The trained machine learning models are then deployed in a production
environment, where they can be used to make real-time predictions about solar
power generation. The results show that the proposed approach achieves a 99%
AUC for solar power generation prediction, which can help energy companies
better manage their solar power systems, reduce costs, and improve energy
efficiency.
