Load Forecasting and Planning Optimization of Low Voltage Distribution Network Based on Deep Learning

Abstract This study develops a unified framework for accurate load forecasting and planning optimization in low-voltage distribution networks, addressing challenges from load volatility and renewable integration. We propose an Adaptive DeepTemporalNet model, which incorporates a Dynamic Feature Capture Module (DFCM) and an Adaptive Weight Adjustment Module (AWAM), enabling simultaneous modeling of short- and long-term load dependencies while dynamically reweighting critical features. Experiments on a three-year dataset show that Adaptive DeepTemporalNet reduces mean squared error (MSE) by 32% compared to LSTM and over 50% compared to ARIMA, achieving an average mean absolute percentage error (MAPE) of 3.5%. Furthermore, by integrating load forecasting with a stochastic chance-constrained optimization framework, the proposed approach lowers total planning costs by about 15% and improves voltage compliance rates by up to 12 percentage points. These results demonstrate both theoretical contributions—advancing adaptive temporal modeling and stochastic optimization—and practical benefits for grid operators seeking stable, cost-effective, and renewable-friendly network operation.