Supplemental Materials
Table A.1 Set Point Assumptions of Studies
Study
Set Point(s)
Amato et al.
2005
12.8C
(commercial);
15C (residential)
16.1C (all
countries);
22.4C (warmer
countries); 14.7C
(colder countries
18C
Bessec and
Fouquau 2008
Day 2006
Determination
Method
Endogenous
optimization
Form of
Relationship
Symmetric
linear
Endogenous
optimization
Logistic/
Exponential
(exponential
preferred)
15 European
countries, 1985-2000,
monthly
Exogenous
assumption
Exogenous
assumption
Symmetric
linear
Non-linear
cubic
smoothing
splines
Symmetric
linear
U.K.
Engle et al.
1986
18C
Eskeland and
Mideksa 2010
18-22C
Exogenous
assumption
Franco and
Sanstad 2008
12C
Endogenous
optimization
Cubic
Gupta 2012
20-22C (20002005)
18.5-20C (20052009)
18C
Endogenous
optimization
Non-linear
cubic
smoothing
splines
Linear
symmetric
Non-linear
kernel
smoothing
Logistic curve
Hekkenberg et
al. 2009
Henley and
Peirson 1997
20-24C
Exogenous
assumption
Endogenous
optimization
Lee and Chiu
2011
12C
Endogenous
optimization
Mirasgedis et
al. 2006
18.5C
Endogenous
optimization
Cubic
Moral-Carcedo
and VicénsOtero 2005
15.4C (preferred
model);
15.5-18.4C (two
threshold model)
Endogenous
optimization
Logistic curve
1
Context
Massachusetts 19772001, monthly
21 monthly billing
cycles for 4 U.S.
utilities
European cities and
countries, annual
consumption, 19952005
California daily
consumption, 20042005
New Delhi daily
consumption, 20002009
Simulated dataset
U.K. time-of-use
participants, April
1989 – March 1990
24 OECD countries’
annual data from
1978-2004
Greek hourly
consumption from
1993-2002
Spanish daily
electricity
consumption from
August 1995 –
August 2003
Study
Set Point(s)
Ruth and Lin
2006
D. J. Sailor
2001
15C (residentialelectricity);
12C
(commercialelectricity);
22C (NG);
18C (Fuel Oil)
18.3C /21C
(Florida only)
Valor et al.
2001
15-21C (18C
preferred)
Determination
Method
Endogenous
optimization
Form of
Relationship
Symmetric
linear
Endogenous
optimization
Asymmetric
linear
Exogenous
assumption
Asymmetric
linear
Context
Maryland 1977-2001,
monthly
8 states monthly
consumption, 19821994
Spanish daily
electricity
consumption from
January 1983 – April
1999
References
Engle, R. F., Granger, C. W. J., Rice, J., & Weiss, A. (1986). Semiparametric Estimates of the
Relation Between Weather and Electricity. Journal of the American Statistical Association,
81(394), 310–320.
Eskeland, G. S., & Mideksa, T. K. (2010). Electricity demand in a changing climate. Mitigation
and Adaptation Strategies for Global Change, 15(8), 877–897. doi:10.1007/s11027-0109246-x
Henley, A., & Peirson, J. (1997). Non-Linearities in Electricity Demand and Temperature:
Parametric Versus Non-Parametric Methods. Oxford Bulletin of Economics and Statistics,
59(1), 149–162.
Lee, C.-C., & Chiu, Y.-B. (2011). Electricity demand elasticities and temperature: Evidence from
panel smooth transition regression with instrumental variable approach. Energy Economics,
33(5), 896–902. doi:10.1016/j.eneco.2011.05.009
Moral-Carcedo, J., & Vicéns-Otero, J. (2005). Modelling the non-linear response of Spanish
electricity demand to temperature variations. Energy Economics, 27(3), 477–494.
Sailor, D., & Pavlova, A. (2003). Air conditioning market saturation and long-term response of
residential cooling energy demand to climate change. Energy, 28(9), 941–951.
Ruth, M., & Lin, A.-C. (2006). Regional energy demand and adaptations to climate change:
Methodology and application to the state of Maryland, USA. Energy Policy, 34(17), 2820–
2833.
2
Valor, E., Meneu, V., & Caselles, V. (2001). Daily Air Temperature and Electricity Load in
Spain. Journal of Applied Meteorology and Climatology, 40(8), 1413–1421.
Yun, G. Y., & Steemers, K. (2011). Behavioural, physical and socio-economic factors in
household cooling energy consumption. Applied Energy, 88(6), 2191–2200.
3