首页|Studies from School of Economics and Management in the Area of Artificial Intell igence Described (Optimization strategy of property energy management based on a rtificial intelligence)
Studies from School of Economics and Management in the Area of Artificial Intell igence Described (Optimization strategy of property energy management based on a rtificial intelligence)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on artificial intelligence is now available. According to news reporting from the School of Economics and Management by NewsRx journalists, research stated, “This study focuses on the de sign and optimization of property energy management systems, aiming to improve e nergy efficiency, reduce waste, and enhance user comfort and satisfaction throug h intelligent means. The research background is based on the urgency of energy c onservation and emission reduction, and the rise of smart property management mo dels on a global scale, especially the increasing demand for energy efficiency m onitoring, predictive analysis, automated control, and user engagement.” Our news reporters obtained a quote from the research from School of Economics a nd Management: “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-fri endly interfaces, and system safety. An empirical case study was conducted at a large-scale commercial complex, confirming the effectiveness of the system. Thro ugh intelligent transformation, specifically the optimization of air conditionin g 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 s ignificantly improved. During the research process, several limitations and chal lenges were encountered, including data quality issues and scalability concerns. These limitations were addressed through rigorous data preprocessing and valida tion, ensuring the robustness of the findings and their applicability to similar environments. The results demonstrate the potential of integrating artificial i ntelligence and machine learning techniques into property energy management syst ems, paving the way for more sustainable and efficient buildings.”
School of Economics and ManagementArti ficial IntelligenceEmerging TechnologiesMachine LearningTechnology
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