Research on energy efficiency diagnosis of integrated energy intelligent platform

This study centers on enhancing the energy efficiency diagnostics of an integrated smart energy platform. It seeks to boost the platform's accuracy and practicality in predicting energy consumption and analyzing energy efficiency by incorporating a PSO-BP neural network model. The research demonstrates that this model effectively manages the nonlinear relationships within complex energy systems, enabling precise energy consumption forecasts. Verification of the model indicates a strong alignment between predicted and actual energy use, with minimal error, underscoring its high level of accuracy and dependability. Furthermore, the platform's architecture has been refined, featuring the integration of edge computing and enhancements to the communication framework, which greatly improve real-time monitoring and data processing. In summary, this work introduces an innovative approach to the energy efficiency assessment of integrated smart energy platforms, aiding businesses in their efforts to conserve energy and reduce emissions while advancing intelligent and precise energy management practices.