Distributed buildings energy storage charging load forecasting method considering parallel deep learning model

摘要

At present, with total building energy consumption accounts for about 21.33% of the energy consumption of social terminals, the building energy consumption has a tendency to continue to grow. According to the geographical location of users and the local weather conditions, we analyze the energy resources available to users and design a multienergy complementary coupling system. We have taken full account of the use of renewable energy and recovery of waste heat resources. Large-scale increase of electrical equipment, a large number of charging load access to the grid, the power system planning, operation and operation of the electricity market will have a profound impact. The current mode of the supercapacitor and the DC bus determines the mode of operation of the converter. Based on a detailed analysis of each working mode, we design a corresponding control scheme and achieve a smooth transition and switching between modes. Simulation and experiment verify the correctness and effectiveness of the converter and hybrid energy storage control strategy.