Evaluation of Machine Learning Techniques for Green Energy Prediction
This work addresses energy prediction for renewable energy systems, but appears incremental as it evaluates existing methods without introducing new approaches.
The paper evaluated multiple machine learning techniques (Bayesian Inference, Neural Networks, Support Vector Machines, PCA) for predicting green energy (wind and solar) using historical weather data and forecasts, aiming to predict energy availability, deviations, correlations, and recover missing data.
We evaluate the following Machine Learning techniques for Green Energy (Wind, Solar) Prediction: Bayesian Inference, Neural Networks, Support Vector Machines, Clustering techniques (PCA). Our objective is to predict green energy using weather forecasts, predict deviations from forecast green energy, find correlation amongst different weather parameters and green energy availability, recover lost or missing energy (/ weather) data. We use historical weather data and weather forecasts for the same.