Truncated-Regularized Kernel Ridge Regression Hybrid Model for Solar Irradiance Prediction and Renewable Energy Forecasting

Abstract

The significance of Solar Irradiance Prediction & Renewable Energy Forecasting for efficient energy utilisation is highlighted by the fast adoption of renewable energy systems, which is being propelled by falling PV costs and rising fossil fuel prices. Solar energy sources are beneficial to the environment, but they are difficult to predict because of their inherent variability and non-stationary behaviour. In response, the research suggests a solid method for predicting solar irradiance, which is integral to PV power generation. Dimensionality reduction using PCA follows extensive data pretreatment, which includes outlier removal and normalisation. The next step is to create a hybrid predictive model that forecasts both the short and long term using a TRKRR fast-trainable statistical learning approach using a variety of historical meteorological information. In testing, the suggested TRKRR-based model achieved a minimum MSE of 0.00142, demonstrating that it outperforms previous algorithm-trained models, especially in extremely dynamic weather situations. With the presentation of a scalable, accurate, and dependable model that can handle variability in renewable energy data, this research concludes that Solar Irradiance Prediction & Renewable Energy Forecasting has made substantial advancements, paving the way for better integration of solar power into modern energy systems.