Research Article
Exploring Machine Learning Models for Solar Energy Output Forecasting
@INPROCEEDINGS{10.1007/978-3-031-48888-7_18, author={Idamakanti Kasireddy and V Mamatha Reddy and P. Naveen and G Harsha Vardhan}, title={Exploring Machine Learning Models for Solar Energy Output Forecasting}, proceedings={Cognitive Computing and Cyber Physical Systems. 4th EAI International Conference, IC4S 2023, Bhimavaram, Andhra Pradesh, India, August 4-6, 2023, Proceedings, Part I}, proceedings_a={IC4S}, year={2024}, month={1}, keywords={Solar production Algorithms Jupyter Note book}, doi={10.1007/978-3-031-48888-7_18} }- Idamakanti Kasireddy
V Mamatha Reddy
P. Naveen
G Harsha Vardhan
Year: 2024
Exploring Machine Learning Models for Solar Energy Output Forecasting
IC4S
Springer
DOI: 10.1007/978-3-031-48888-7_18
Abstract
Engineering, science, health, and other fields have all used machine learning algorithms. The idea of machine learning is used in this study to forecast solar energy output. Predicting solar energy, a well-known renewable source with a number of advantages, can help with energy consumption planning. Inconsistent weather makes it difficult for grid operators to manage solar energy output, which makes it harder to satisfy customer demand. Utilizing various algorithms including Lasso, Ridge, Linear and Support Vector Regression (SVR) algorithms, our suggested strategy entails developing prediction models. These algorithms produce forecasts based on past weather information such as temperature, dew point, wind, cloud cover, and visibility. SVR algorithm outperformed the other algorithms, according to the Jupyter Notebook examination.
