Digital standard system and adaptive algorithm for intelligent operation of power dispatching

With the growth of computer technology, the application of deep learning (DL) methods in the field of power load forecasting is becoming increasingly widespread. Against the backdrop of growth of smart grids, power system (EPS) scheduling is moving towards intelligence. Intelligent scheduling not only requires accurate load forecasting results, but also requires forecasting technology to adapt to challenges such as massive data processing, complex dynamic changes, and real-time requirements. Therefore, it is particularly important to build a digital standard system for intelligent operation of power dispatching. This article conducts research on the above-mentioned issues and proposes a DL based power load forecasting model. This model achieves high-precision prediction of short-term electricity load by mining potential patterns in historical load data and combining multidimensional influencing factors. The results indicate that the proposed model has high prediction accuracy and stability, and can provide reliable basis for EPS scheduling decisions. By arranging power dispatch plans reasonably, the operational efficiency and stability of EPS can be effectively improved, laying a solid foundation for the development of smart grids.