Abstract
The study relies on the provincial smart energy service platform of Jiangsu Province, optimizes its accurate prediction module of energy consumption and the energy efficiency improvement module, and proposes an efficiency improvement strategy based on the two modules of the platform. The accurate prediction module adopts the data processing and feature analysis method based on STL-MIC and builds a deep learning framework based on CNN-BiLSTM. The projected predicted data is then input to the energy efficiency improvement module, which employs the method of flexible load scheduling to achieve the best configuration of demand-side resources.
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