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The goal of the Distributed Intelligent Automated Demand Response (DIADR) project at UC Berkeley is to reduce peak load of Sutardja Dai Hall by 30% while maintaining a healthy, comfortable, and productive environment for the occupants. The team consists of members from Siemens, This paper describes the work in progress of achieving this goal in the office(More)
Accurate load prediction methods in commercial buildings can provide a benefit to understanding the energy behavior of commercial buildings for improved building energy management. A crucial first step to understanding the potential energy savings and to developing a proper building energy management system in commercial buildings is the development of a(More)
We present the architecture, implementation, and initial results from a live, continuous energy auditing system. By combining QR codes and mobile phones, we are able to iteratively collect building plug-load information and couple it with live, streaming meter data. The system consists of a deployment of inexpensive tags (QR codes) as well a(More)
In this paper we demonstrate a proof of concept that quickly linking and modifying parameters of a user based simulation driven by real device audit data is possible. Further, we demonstrate an ability to simulate basic functions and behavior of users and their appliances in an office building and have made a solid case for the continuation of this work(More)
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