Enhancing Renewable Energy Forecasting Through Adaptive Statistical Modeling and Optimization Strategies

Managing energy resources effectively involves a profound awareness of factors governing consumption behavior, specifically in renewable integration scenarios. With the transition towards the cleaner power grid, demand and supply balancing become ever more complex, and high-level computational approaches must therefore be embraced. This study addresses renewable energy consumption forecast using machine learning (ML) models optimized with the Red Fox Optimization Algorithm (RFOA) with a view to enhancing predictive generalizability and stability for optimal operation under changing scenarios. For this propose, the study compare the performance of six optimized ML algorithms Elastic Net, Extreme Gradient Boosting (XGBoost), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), and Histogram-Based Gradient Boosting (HGB). Comparative analysis demonstrates that RFOA-ElasticNet, with the high R² of 0.9859 on the training set and 0.9481 on the test set, indicates excellent explanatory power and generalization. Furthermore, this model with MAE and MASE of 93.24 and 0.2603 on the test data confirms its reliable predictive accuracy with minimal prediction errors. Aside from predictive performance, an understanding of driving factors in consumption is critical in improving model interpretability. Based on the feature importance analysis, it is determined that certain external factors, including carbon intensity in electricity generation with an impact factor of around 140, have been discovered to act as principal drivers of consumption trends. These findings yield useful information for operators, professionals, and policymakers for planning at a strategic level.