# Data-Driven Evaluation of Building Demand Response Capacity

arXiv: https://arxiv.org/abs/1602.02868 · HTML: https://mlsanity.com/paper/1602.02868/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2016.

## Built on by (most cited first, 1 of 1)

- [Machine Learning for Smart and Energy-Efficient Buildings](https://mlsanity.com/paper/2211.14889/) (2022)

## Abstract (arXiv)

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.