用低成本物联网设备实时监测温感与空调能耗，助力节能行为改变。
Development of Low-Cost IoT Units for Thermal Comfort Measurement and AC Energy Consumption Prediction System
- 基于树莓派4B+构建低成本系统，实时采集温湿度与空调设定温度。
- 机器学习模型预测能耗变化，R²达97%，准确率高。
- 通过移动/桌面应用反馈数据，适合办公室节能管理场景。
为应对建筑领域巨大的能源消耗，日本政府于2019年启动了BI-Tech（行为洞察×技术）项目，旨在通过人工智能与物联网技术促进自愿性节能行为。本研究针对中小型办公建筑，提出一种基于低成本物联网的BI-Tech系统，采用Raspberry Pi 4B+平台实现实时监测室内热环境及空调设定温度。结合机器学习与图像识别技术，系统分析数据以计算PMV指数，并预测因温度调节引起的能耗变化。移动端与桌面端应用将信息传达给用户，推动节能行为调整。机器学习模型取得R²值为97%的优异表现，验证了该系统在促进用户节能习惯方面的有效性。
原文摘要 · Abstract (English)
In response to the substantial energy consumption in buildings, the Japanese government initiated the BI-Tech (Behavioral Insights X Technology) project in 2019, aimed at promoting voluntary energy-saving behaviors through the utilization of AI and IoT technologies. Our study aimed at small and medium-sized office buildings introduces a cost-effective IoT-based BI-Tech system, utilizing the Raspberry Pi 4B+ platform for real-time monitoring of indoor thermal conditions and air conditioner (AC) set-point temperature. Employing machine learning and image recognition, the system analyzes data to calculate the PMV index and predict energy consumption changes due to temperature adjustments. The integration of mobile and desktop applications conveys this information to users, encouraging energy-efficient behavior modifications. The machine learning model achieved with an R2 value of 97%, demonstrating the system's efficiency in promoting energy-saving habits among users.
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