Quantifying Changes in Building Electricity Use, With Application to Demand Response

By Johanna L. Mathieu, P. Price, S. Kiliccote, M. Piette. IEEE Transactions on Smart Grid, 2011

350 citations recorded by the source corpus.

Subfields: Building and Construction, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/79fad0569a384e156cd563ceaf952d317ce2d802

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

1. Saifur Rahman
2. Manisa Pipattanasomporn
3. Mengmeng Cai

In Electrical and Electronic Engineering

1. Saifur Rahman
2. Manisa Pipattanasomporn
3. Mengmeng Cai

Citations in the corpus

• Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques (2019, background): “From the perspective of electric utilities, accurate building-level load forecasts ensure the e ff ectiveness of both pre-DR resource allocation and post-DR performance evaluation (i.e., baseline identi fi cation) [10,11].”