Relating residential and commercial sector electricity loads to climate : evaluating state level sensitivities and vulnerabilities
By David J. Sailor. 2001
254 citations recorded by the source corpus.
Subfields: Building and Construction, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/19d52fa15b16c24260946db5dfd3cfab1f9bf306
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
- Ching-Lai Hor
- Kashem M. Muttaqi
- Julien Fouquau
- Marie Bessec
- Ashish Prakash Agalgaonkar
- S. Majithia
- Simon J. Watson
- Dao H. Vu
In Electrical and Electronic Engineering
- Kashem M. Muttaqi
- Ashish Prakash Agalgaonkar
- Dao H. Vu
- Ching-Lai Hor
- Julien Fouquau
- Marie Bessec
- S. Majithia
- Simon J. Watson
Citations in the corpus
- A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables (2015, methodology): “The authors in [19] 56 have restated the importance of these variables by building electricity demand models using five specific variables namely 57 cooling degree days (CDD), heating degree days (HDD), humidity, wind speed and the enthalpy latent days (ELD) for different 58 States of USA.”
- Effects of long-term climate change on global building energy expenditures (2018, background): “…electricity consumption in eight tute of Science and Technology ty. f Geological and Atmospheric icle under the CC BY license (http://c U.S. states (Sailor, 2001), cooling energy demand in 12 U.S. cities (Sailor and Pavlova, 2003), total building energy consumption in five Chinese cities (Wan et…”
- Indications for a changing electricity demand pattern: The temperature dependence of electricity demand in the Netherlands (2009, background): “Most studies investigating this relation assume a fixed balance temperature (Sailor and Mun ˜ oz, 1997; Sailor, 2001; Amato et al., 2005; Crowley and Joutz, 2005; Hadley et al., 2006; Ruth and Lin, 2006), mostly at 181C.”
- Analyzing the impact of weather variables on monthly electricity demand (2005, methodology): “To calculate the HDD value, a daily mean temperature is estimated based on the average of the minimum and maximum temperatures for that day [15].”
- The non-linear link between electricity consumption and temperature in Europe: A threshold panel approach (2008, methodology): “3Alternative threshold values are sometimes considered: 21˚C for the state of Florida in Sailor and Munoz (1997) and Sailor (2001), 10˚C and 25˚C for Israel in Beenstock et al. (1999), 18˚C and 22 ˚C for Cyprus in Zachariadis and Pashourtidou (2007).”