Global energy demand surge and worsening environmental issues make optimising corporate energy management key to boosting efficiency, cutting costs and achieving sustainability.This study proposes a multi-level, modular decision support system (DSS) architecture for enterprise energy management optimisation, based on big data analysis.It integrates deep learning, reinforcement learning and digital twins, using genetic algorithms (GA) for global search (e.g., multi-energy allocation) and particle swarm optimisation (PSO) for faster-convergent local refinement.Lighting systems account for 47% of production auxiliary energy; office devices take 63.9% of administrative consumption.A 500+-device factory saw energy utilisation rise from 82% to 92% via the system.Analysis shows production-linked consumption (52.7% of total) has core equipment contributing 88.3%.
Zhidan Jiang (Thu,) studied this question.