Solar Power Prediction Using Machine Learning

TL;DR

This paper introduces a machine learning approach that predicts solar power generation with 99% accuracy, utilizing data preprocessing, feature selection, and multiple algorithms for deployment in real-time energy management.

Contribution

It presents a comprehensive machine learning framework for high-accuracy solar power prediction, including data handling, feature selection, and deployment strategies.

Findings

Achieved 99% AUC in solar power prediction

Effective data preprocessing and feature selection improve model accuracy

Models are successfully deployed for real-time predictions

Abstract

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.