Data-Driven Evaluation of Building Demand Response Capacity
| Autor: | William G. Temple, Varun Badrinath Krishna, Deokwoo Jung, David K. Y. Yau |
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| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Demand response
Measure (data warehouse) Load management Smart grid Operations research Computer science Control (management) FOS: Electrical engineering electronic engineering information engineering Probabilistic logic Key (cryptography) Computer Science - Systems and Control Systems and Control (eess.SY) Data-driven |
| Zdroj: | SmartGridComm Publons |
| Popis: | Before a building can participate in a demand response program, its facility managers must characterize the site's ability to reduce load. Today, this is often done through manual audit processes and prototypical control strategies. In this paper, we propose a new approach to estimate a building's demand response capacity using detailed data from various sensors installed in a building. We derive a formula for a probabilistic measure that characterizes various tradeoffs between the available demand response capacity and the confidence level associated with that curtailment under the constraints of building occupant comfort level (or utility). Then, we develop a data-driven framework to associate observed or projected building energy consumption with a particular set of rules learned from a large sensor dataset. We apply this methodology using testbeds in two buildings in Singapore: a unique net-zero energy building and a modern commercial office building. Our experimental results identify key control parameters and provide insight into the available demand response strategies at each site. In proceedings of the 2014 IEEE International Conference on Smart Grid Communications (IEEE SmartGridComm 2014) |
| Databáze: | OpenAIRE |
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