标题： 低碳智能建筑的能耗优化与自供电环境监测设计 显示英文标题

标题： Energy consumption optimization and self-powered environmental monitoring design for low-carbon smart buildings

摘要： 尽管智能建筑作为可持续城市发展的基石日益受到重视，但由于设计不佳、材料选择不当和用户行为问题，仍然存在显著的能源低效率。 集成建筑信息模型（BIM）和太阳能环境监测系统在低碳智能建筑中的能源优化应用仍缺乏深入研究。 基于BIM的设计改进结合光伏系统能否实现显著的节能效果，并实现自主供电的环境监测？ 本研究对广东省中国一座改造后的小学建筑进行了案例分析，利用基于BIM的能源模拟、材料优化和太阳能技术集成。 结果表明，所提出的方法将年能耗降低了40.68%，其中照明能耗降低了36.59%。 屋顶光伏系统实现了7.46年的投资回收期，同时能够自主为环境传感器供电。 硬件系统集成了传感器和一个基于ARDUINO的控制器，用于检测降雨量、温度和空气质量等环境因素。 它由一个6W的太阳能板和一个2200 mAh/7.4 V的锂电池供电，以确保稳定运行。 本研究强调了BIM与太阳能能源集成的潜力，可以将传统建筑转变为节能、自我维持的智能结构。 进一步的研究可以扩大这些方法在不同气候和建筑类型中的可扩展性。 显示英文摘要

摘要： Despite the growing emphasis on intelligent buildings as a cornerstone of sustainable urban development, significant energy inefficiencies persist due to suboptimal design, material choices, and user behavior. The applicability of integrated Building Information Modeling (BIM) and solarpowered environmental monitoring systems for energy optimization in low-carbon smart buildings remains underexplored. Can BIM-driven design improvements, combined with photovoltaic systems, achieve substantial energy savings while enabling self-powered environmental monitoring? This study conducts a case analysis on a retrofitted primary school building in Guangdong, China, utilizing BIM-based energy simulations, material optimization, and solar technology integration. The outcomes reveal that the proposed approach reduced annual energy consumption by 40.68%, with lighting energy use decreasing by 36.59%. A rooftop photovoltaic system demonstrated a payback period of 7.46 years while powering environmental sensors autonomously. Hardware system integrates sensors and an ARDUINO-based controller to detect environmental factors like rainfall, temperature, and air quality. It is powered by a 6W solar panel and a 2200 mAh/7.4 V lithium battery to ensure stable operation. This study underscores the potential of BIM and solar energy integration to transform traditional buildings into energy-efficient, self-sustaining smart structures. Further research can expand the scalability of these methods across diverse climates and building typologies.