高质量的入住率数据是建筑能耗模拟的关键输入参数，对建筑能耗模型性能的精度和准确性有很大影响。然而，当前通过占用检测技术获取此类数据的方法要么需要实现大规模传感器网络，要么需要复杂且耗时的计算算法，这严重限制了实时占用数据在建筑能耗模拟中的应用。本文研究了基于移动互联网的定位数据是否以及如何有利于建筑能耗模拟。本文首先简要总结了几种主流占用率检测方法的优缺点。然后，介绍了基于移动互联网的乘客检测方法的原理。以上海某复杂建筑的初始能耗性能模型为例，利用建筑整体仿真软件，验证了该方法的有效性。 使用楼宇自动化系统的历史数据进行初始校准，然后采用两种平行的进一步校准方法。第一种方法使用传统的校准方法（即基于试错的方法），第二种方法使用基于移动互联网的入住率数据。仿真结果表明，与传统的试错法相比，基于移动互联网的入住率数据有助于提高建筑模型的预测精度，简化校准过程。引用:2018年年度会议，德克萨斯州休斯顿，会议论文

High-quality occupancy data is the key input parameter for building energy simulations and has a big impact on the precision and accuracy of building energy model performance. However, current approaches to get such data through the occupancy detection technology require either implementations of large-scale sensors network or sophisticated and time-consuming computational algorithms, which strongly limits the application of real-time occupancy data for building energy simulation. This paper presents an investigation of whether and how mobile-internet-based positioning data can benefit building energy simulation. This paper first briefly summarizes the pros and cons of several mainstream occupancy detection methods. Then, the principles of the proposed mobile-internet-based occupant detection method are introduced. An initial energy performance model of a complex building in Shanghai, China with a whole building simulation software is used as a case study to demonstrate the effectiveness of the proposed method. An initial calibration is conducted using the history data from the building automation system, followed by two parallel further calibration approaches. The first approach is conducted with a traditional calibration method (i.e., trial-and-error based method) and the second one uses the mobile-internet-based occupancy data. The simulation results show that using mobile-internet based occupancy data can help improve the building model prediction accuracy and simplify the calibration process compared with traditional trial-and-error approaches.