RECORD
Data-Driven Evaluation of Building Demand Response Capacity
Systems and Control · Submitted 9 Feb 2016
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Our experimental results identify key control parameters and provide insight into the available demand response strategies at each site.
The compiled record
The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
Open the compiled record
The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Before a building can participate in a demand response program, its facility managers must characterize the site's ability to reduce load. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | Today, this is often done through manual audit processes and prototypical control strategies. | background 0.98 | 0.04 | 0.01 | asserted 0.91 |
| s2 | 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. | contribution 0.98 | 0.77 | 0.10 | asserted 0.96 |
| s3 | 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). | method 0.63 | 0.96 | 0.12 | asserted 1.00 |
| s4 | 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. | method 0.90 | 0.96 | 0.06 | asserted 1.00 |
| s5 | We apply this methodology using testbeds in two buildings in Singapore: a unique net-zero energy building and a modern commercial office building. | method 0.74 | 0.95 | 0.04 | asserted 0.98 |
| s6 | Our experimental results identify key control parameters and provide insight into the available demand response strategies at each site. | result 0.86 | 0.97 | 0.47 | asserted 1.00 |
What was examined, and what was not
- arXiv listing abstract v1
- full text
- figures and tables
- references and citing sentences
- relations to other papers
- P abstract
- S sections
- T body sentences
- C citations
- R cross-paper relations
The paper
Original abstract
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.
Human review
Discussion
Know something about this paper? Leave a comment, or highlight any passage above to comment on it.