LOAD FORECASTING AND EQUIPMENT OPTIMIZATION IN RENEWABLE ENERGY INTEGRATION
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Abstract
This paper presents a two-layer integrated framework for load forecasting and equipment optimization under renewable energy integration. The first layer delivers day-ahead and near real-time forecasts using a hybrid LSTM–Transformer model trained on SCADA/AMI data enriched with weather and calendar features. The second layer performs multi-objective MILP optimization combined with Model Predictive Control (MPC) to coordinate capacitor banks, voltage regulators/OLTC, inverter PQ dispatch, demand response (DR) signals, and energy storage, while explicitly accounting for forecast uncertainty. Simulation results indicate reduced technical losses and voltage violations, lower renewable curtailment, transformer loading relief, and improved operational costs. By unifying the forecast–control loop within a smart-grid context, the proposed approach enhances network resiliency and overall energy efficiency.
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