Building energy efficiency management is crucial for sustainable development amid global energy challenges.This study integrates big data analytics and artificial intelligence to develop an intelligent scheduling system for building energy optimisation.Using long short-term memory (LSTM) networks, a deep learning model was trained on multi-source data including energy consumption, weather forecasts, and pedestrian flow, achieving over 95% prediction accuracy.The system dynamically adjusts building equipment operations based on predictive outcomes, reducing overall energy consumption by 20%.Experimental results demonstrate significant economic benefits and enhanced energy efficiency.The research also explores broader applications of AI in energy management, such as equipment failure prediction and performance evaluation.This work provides a novel technological pathway for green building development and supports global sustainability goals.
Ming Qiu (Thu,) studied this question.