占用率在建筑物中使用的能源量中起着重要作用，其存在本质上是随机的。有大量证据表明，建筑的性能通常不如能源模拟预测的好。使用不切实际的入住率数据作为建筑能耗模型（BEM）的输入是其背后的一个主要原因。因此，预测的能源性能与实际的能源性能之间存在很大差异。在本研究中，默认入住率输入下，其范围在29.8-43.2%之间。本研究旨在通过实际应用，提高现有校园建筑能耗模型的准确性- 时间占用表和计量用电量数据。首先，使用180天（一学年中的一个学期）测量的每小时耗电量（插头负载和照明）kWh和教室、演讲厅和计算机室等空间的占用率数据来制定每日楼层占用率曲线。一种通用、可行且低成本的基于Wi-Fi的占用率检测解决方案为建筑物提供可靠的每小时占用率信息。K-均值和小时平均聚类技术用于确定入住情况。现有的边界元模型用这种占用模式进行了进一步校准。 使用最大聚类平均值的小时平均值绘制的入住率曲线显示，BEM预测精度的误差最小，低至6.9%，符合ASHRAE指南14（CVRMSE<15%）的日常数据。引用:2017年年度会议，加利福尼亚州长滩，会议论文

Occupancy plays a significant role in the amount of energy used in buildings and their presence is stochastic in nature. There is extensive evidence to suggest that buildings usually do not perform as well as predicted by energy simulation. Use of unrealistic occupancy data as an input of Building Energy Model (BEM) is a major reason behind it. Thus, large discrepancies are being observed between predicted and actual energy performance. In this study, it ranges between 29.8-43.2% with default occupancy input. The research aims to increase the accuracy of existing campus building energy model through real-time occupancy schedule and metered electricity consumption data. Firstly, 180 days (a semester in an academic year) of the measured hourly electricity consumption (Plug load and lighting) kWh and occupancy data for spaces like a classroom, lecture theater and computer room are used to develop a daily floor wise occupancy profile. A generic, feasible and low-cost Wi-Fi based occupancy detection solution provides reliable hourly occupancy information in buildings. K-means and hourly average clustering techniques are used to identify occupancy profile. The existing BEM model is further calibrated with this occupancy pattern. Occupancy profile developed with an hourly average of the largest cluster mean shows the least error in BEM prediction accuracy, which is as low as 6.9% and within ASHRAE Guideline 14 (CVRMSE < 15%) for daily data.