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This study aims to optimize energy efficiency in green buildings using artificial intelligence algorithms.

Artificial Intelligence-Based Energy Efficiency Optimization and Intelligent Control System for Green Buildings

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Randomized trial demonstrates energy optimization in green buildings, suggesting improved efficiency outcomes.

This study aims to optimize energy efficiency in green buildings using artificial intelligence algorithms.
Developed an intelligent control system with a four-step approach: data acquisition, modeling, dynamic optimization, and feedback adaptation.
Utilized LSTM models to predict energy consumption based on environmental parameters and user behavior.
Implemented a real-time feedback mechanism for adjusting operational parameters.
The experimental group achieved an average energy consumption of 174.5 kWh/day compared to 218.4 kWh/day in the control group.
Dynamic adaptation of control strategies resulted in significant energy savings, minimizing redundant consumption.
The new system demonstrated enhanced accuracy and response time compared to traditional methods.

Dong Wang (2026) studied this question.

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