An End-to-End Learning Model With Quantization Technology for Dynamic Prediction-Decision Joint-Making in Active Distribution Networks

Efficient load management and system stability in active distribution networks (ADNs) depend heavily on accurate load forecasting and dynamic decision-making. This paper proposes a novel end-to-end joint learning model for dynamic prediction-decision joint-making in active distribution networks. The model integrates a prediction module, leveraging long short-term memory networks to forecast photovoltaic (PV) generation, active power load, and reactive power load, with a control decision module based on Twin Delayed Deep Deterministic Policy Gradient (TD3). The forecasting outputs are fed into the decision-making module, enabling dynamic adjustments to network parameters. A feedback mechanism is introduced that incorporates decision losses into the prediction module to enhance forecasting accuracy, thereby creating a tightly coupled, end-to-end joint optimization framework. In addition, a mixed precision quantization technology is adopted to reduce computational overhead and facilitate efficient deployment. Experimental results on distribution networks demonstrate that the proposed model outperforms other methods in terms of load forecasting accuracy and dynamic control effectiveness, resulting in substantial improvements in the operational efficiency and stability of ADNs.