Gökhan Yüksek: Operational Implications of Renewable Power Forecasting in Modern Grids

The rapid rise in the share of renewable energy generation in power systems has made accurate short- and medium-term power forecasting a fundamental requirement for modern grids. Wind and photovoltaic (PV) power production exhibit strong variability driven by meteorological conditions and temporal patterns, creating significant challenges for generation scheduling, reserve allocation, grid stability, and energy market operations. Forecast uncertainty, therefore, directly affects operational costs and planning limitations, driving growing interest in data-driven forecasting approaches.

This study offers a unified perspective on renewable power forecasting through two complementary case studies of wind and solar energy, using optimization-assisted regression modeling. The central view is that forecasting performance depends not only on the selected regression model but also on how effectively the model is configured for a specific site and data regime. Hyperparameter selection and model calibration strongly shape the bias–variance trade-off, sensitivity to rare meteorological events, and prediction robustness under changing operating conditions. In this context, approaches that combine regression-based forecasting models with metaheuristic optimization techniques have been widely reported in the literature as effective for model parameter tuning and generalization. For the wind energy case study, power forecasting is formulated as a supervised regression problem using meteorological and temporal predictors, and optimization-assisted tuning consistently improves accuracy over default model configurations. For the solar PV case study, irradiance-driven power forecasting is investigated under the same methodological framework, revealing distinct drivers and error characteristics compared with wind power, while still delivering similar performance gains. The study also discusses common challenges in renewable power forecasting, including data quality limitations, diurnal and seasonal patterns, and the impact of meteorological feature selection on model stability and interpretability. Forecasting performance is assessed using standard regression metrics, and practical considerations for optimization-based tuning, such as parameter sensitivity and reproducibility, are briefly discussed. Overall, this work indicates that optimization-assisted model tuning is a practical and promising approach for improving forecasting accuracy and supporting more reliable integration of wind and solar generation into modern power grids. The presented perspective is supported by an implemented software environment that enables comparative evaluation of regression models and optimization-assisted tuning strategies.