PRINCIPLES OF INTEGRATING AI-BASED PREDICTIVE MODELS INTO SMART GRIDS FOR LOAD FORECASTING IN THE INTEGRATION OF RENEWABLE ENERGY SOURCES

PRINCIPLES OF INTEGRATING AI-BASED PREDICTIVE MODELS INTO SMART GRIDS FOR LOAD FORECASTING IN THE INTEGRATION OF RENEWABLE ENERGY SOURCES Turaxanov Sherzod Shadiyarovich “RIETER TEXTILSYSTEMEN” LLC Abstract: This paper presents principles for integrating AI-based predictive models into smart grids to improve load forecasting under high penetration of renewable energy sources (RES). We outline a modular architecture that couples data acquisition (AMI/SCADA, weather and market feeds) with a unified feature store and an MLOps pipeline for model training, validation, deployment, and monitoring. The methodology prioritizes hybrid approaches—combining physics-informed constraints with deep learning (e.g., LSTM/Temporal-Fusion/Transformer) and topology-aware graph neural networks—to capture non-stationary demand patterns and spatio-temporal effects induced by distributed generation. We formalize uncertainty management through probabilistic forecasting (quantiles and full predictive distributions), calibration tests, and decision metrics aligned with grid operations (MAPE, nRMSE, pinball loss, CRPS). Model governance emphasizes drift detection, explainability (SHAP-based attribution), and safety guards to enforce operational limits. The principles are demonstrated through scenario design covering (i) variable RES ramping, (ii) demand response activation, and (iii) sensor dropouts, showing how forecasts feed dispatch, storage scheduling, and market bids. The proposed framework provides actionable guidelines—data readiness, model selection, uncertainty quantification, and lifecycle governance—that enable grid operators to realize measurable gains in reliability, flexibility, and cost efficiency when integrating AI-driven forecasts into real-time decision making. Keywords: Smart grid; Load forecasting; Renewable energy integration; AI-based predictive models; Deep learning; Probabilistic forecasting; Physics-informed learning; Graph neural networks; Demand response; MLOps; Uncertainty quantification.