InFLuCs: Irradiance Forecasting Through Reinforcement Learning Tuned Cascaded Regressors

Accurate prediction of solar irradiance is essential for optimizing renewable energy sources in distributed generation systems due to its significant impact on solar power generation. Despite notable advancements, the inherent variability of irradiance presents challenges for existing models. In this article, we introduce a novel approach for irradiance forecasting using a cascaded combination of regressors applied to transformed process variables. Our method utilizes a gradient-boosted decision tree as the primary regressor to generate initial predictions, which are subsequently refined by a support vector regressor acting as an error correction module. Notably, the secondary regressor's kernel, alongside other hyperparameters, is dynamically learned through reinforcement learning with an RNN-based controller. Evaluation results demonstrate that our prediction-correction framework achieves superior performance compared to state-of-the-art approaches, as indicated by RMSE, MAE, and $\text{R}^{2}$ score metrics. Thorough comparative analysis highlights the model's enhanced accuracy and its potential for precise irradiance forecasting.