Energy efficient building environment control strategies using real-time occupancy measurements
Energy efficient building environment control strategies using real-time occupancy measurements
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DOI：
10.1145/1810279.1810284
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发表时间：
2009-11
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通讯作者：
Varick L. Erickson;Yiqing Lin;Ankur Kamthe;R. Brahme;A. Surana;Alberto Cerpa;M. Sohn;S. Narayanan
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文献类型：
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作者：
Varick L. Erickson;Yiqing Lin;Ankur Kamthe;R. Brahme;A. Surana;Alberto Cerpa;M. Sohn;S. Narayanan
目前的气候控制系统通常依赖于建筑物规定的最大占用人数来维持适当的温度。然而，在许多情况下，存在不经常使用的房间，并且可能不必要地加热或冷却。具有关于占用的知识并且能够准确地预测使用模式可以允许通过L-HVAC系统的智能控制来显著节能。在本文中，我们报告部署的无线摄像头传感器网络收集数据占用在一个大型的多功能建筑。该系统估计占用率的准确率为80%。使用从该系统收集的数据，我们构建多变量高斯和代理模型预测用户的移动模式在建筑物。使用这些模型，我们可以预测房间使用情况，从而使我们能够以自适应的方式控制暖通空调系统。我们的模拟表明，通过基于占用估计和使用模式的最佳控制策略，HVAC能源使用量减少了14%。
Current climate control systems often rely on building regulation maximum occupancy numbers for maintaining proper temperatures. However, in many situations, there are rooms that are used infrequently, and may be heated or cooled needlessly. Having knowledge regarding occupancy and being able to accurately predict usage patterns may allow significant energy-savings by intelligent control of the L-HVAC systems. In this paper, we report on the deployment of a wireless camera sensor network for collecting data regarding occupancy in a large multi-function building. The system estimates occupancy with an accuracy of 80%. Using data collected from this system, we construct multivariate Gaussian and agent based models for predicting user mobility patterns in buildings. Using these models, we can predict room usage thereby enabling us to control the HVAC systems in an adaptive manner. Our simulations indicate a 14% reduction in HVAC energy usage by having an optimal control strategy based on occupancy estimates and usage patterns.