Optimization strategy of property energy management based on artificial intelligence

This study focuses on the design and optimization of property energy management systems, aiming to improve energy efficiency, reduce waste, and enhance user comfort and satisfaction through intelligent means. The research background is based on the urgency of energy conservation and emission reduction, and the rise of smart property management models on a global scale, especially the increasing demand for energy efficiency monitoring, predictive analysis, automated control, and user engagement. To address the urgent need for energy conservation and emission reduction, particularly in the realm of property management, this study designed and optimized a property energy management system. The core of the research is a systematic energy management framework that encompasses efficient monitoring, intelligent predictive analytics using techniques such as Long Short-Term Memory (LSTM) networks for energy consumption forecasting, automated control, user-friendly interfaces, and system safety. An empirical case study was conducted at a large-scale commercial complex, confirming the effectiveness of the system. Through intelligent transformation, specifically the optimization of air conditioning and lighting systems using advanced technologies like frequency modulation and LED lighting, a total energy saving rate of 25% was achieved. The annual economic savings exceeded 1.25 million yuan, and user satisfaction was significantly improved. During the research process, several limitations and challenges were encountered, including data quality issues and scalability concerns. These limitations were addressed through rigorous data preprocessing and validation, ensuring the robustness of the findings and their applicability to similar environments. The results demonstrate the potential of integrating artificial intelligence and machine learning techniques into property energy management systems, paving the way for more sustainable and efficient buildings. This revised abstract includes more specific details about the technologies used, such as LSTM networks, and mentions the limitations and challenges faced during the research. It also emphasizes the practical application and scalability of the system.