The temperature sensor deployment for indoor temperature field estimation is critical in managing thermal comfort and achieving energy savings in the digital twin (DT) of building energy systems. Current temperature sensor deployment methods prioritize constructing the true temperature field, which yield increasing cost and added DT computing dimensions. Additionally, they frequently overlooked the significance of information from users' actual usage scenarios for sensor deployment and failed to balance multiple aspects like cost, coverage, and user satisfaction from a user-centric perspective. To solve these issues, a practical, low-cost, and user-centric temperature sensor deployment method leveraging occupancy information is proposed. Based on the first principle, it introduces user information directly from the perspective of improving user satisfaction for deploying temperature sensors. The user-centric multi-objectives temperature sensor deployment model is established by coverage model based on wireless fidelity (WiFi) connection points and user satisfaction metrics. To solve the multi-objective optimization problem with multivariable, constraints, and nonlinear, the Improved Multi-Objective Particle Swarm Optimization (IMOPSO) algorithm is developed. Results show that the proposed method increases WiFi-based coverage and satisfaction metrics. The room-level indoor temperature could be accurately estimated with a steady-state error of 0.199 (average RMSE) and dynamic-state error of 0.298 (heating, average RMSE) based on the proposed deployment method. These results demonstrate that the proposed user-centric approach provides a novel and practical solution for air temperature sensor deployment.

中文翻译：

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一种利用占用信息的数字孪生下以用户为中心的温度传感器部署方法


用于室内温度场估计的温度传感器部署对于管理热舒适度和在建筑能源系统的数字孪生 （DT） 中实现节能至关重要。当前的温度传感器部署方法优先考虑构建真实温度场，这会增加成本并增加 DT 计算维度。此外，他们经常忽视用户实际使用场景信息对传感器部署的重要性，未能从以用户为中心的角度平衡成本、覆盖范围和用户满意度等多个方面。为了解决这些问题，提出了一种实用、低成本、以用户为中心的温度传感器部署方法。基于第一原则，直接从提高用户部署温度传感器满意度的角度引入用户信息。以用户为中心的多目标温度传感器部署模型是基于无线保真 （WiFi） 连接点和用户满意度指标的覆盖模型建立的。为了解决多变量、约束和非线性的多目标优化问题，开发了改进的多目标粒子群优化 （IMOPSO） 算法。结果表明，所提出的方法增加了基于 WiFi 的覆盖率和满意度指标。基于所提出的部署方法，稳态误差为 0.199（平均 RMSE）和动态态误差为 0.298（加热，平均 RMSE），可以准确估计室内室内温度。这些结果表明，所提出的以用户为中心的方法为空气温度传感器的部署提供了一种新颖实用的解决方案。