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Do homes that are more energy efficient consume less energy?
EPRG Presentation
Scott Kelly
18th October 2010
Do homes that are more energy efficient consume less energy?
OutlineMotivation
Context
Review of literature (modeling residential energy)
Data-sources and variables
Simple bivariate regression (the first case)
Overview of Structural Equation Modelling (SEM)
Methodology and application of SEM
Results
Policy implications
Motivation (PhD)Building an energy and CO2 simulation model of the UK residential sector.
Historical data, forward looking, holistic, bottom-up
Technology adoption by households (choice theory)
How this transforms to CO2 savings – how much?
Development of a deterministic engineering thermodynamic model
Assumption that more efficient homes use less energy.
Explain what the dominant factors are that drive residential energy consumption.
OutlineMotivation
Context
Review of literature (modeling residential energy)
Data-sources and variables
Simple bivariate regression (the first case)
Overview of Structural Equation Modelling (SEM)
Methodology and application of SEM
Results
Policy implications
Context
GHG emissions by source in the UK 2008
Total = 627 MtCO 2eq
(MtCO2eq)
GHG emissions by end-use in the UK 2008
Source: DECC (http://decc.gov.uk/en/content/cms/sta tistics/climate_change/data/data.aspx)
73% of UK GHG attributable to household consumption dem and.
(Druckman & Jackson 2009)
Context
Average energy consumption by end-use
Source: UK Domestic Energy Fact File (2006)
of total ENERGY
consumption is used forHEATING
40%
OutlineMotivation
Context
Review of literature (modeling residential energy)
Data-sources and variables
Simple bivariate regression (the first case)
Overview of Structural Equation Modelling (SEM)
Methodology and application of SEM
Results
Policy implications
Review of literature
DeCARB (BREDEM)
BREHOMES
UKDCM
CDEM
OutlineMotivation
Context
Review of literature (modeling residential energy)
Data-sources and variables
Simple bivariate regression (the first case)
Overview of Structural Equation Modelling (SEM)
Methodology and application of SEM
Results
Policy implications
Data from 1996 EHCS and FES‣ English House Condition Survey (EHCS) - 12,131 cases
‣ Fuel and Energy Survey (FES) - 2,531 cases
‣ EHCS contains information on physical properties.
‣ FES contains energy consumption characteristics + metered energy data!!!
‣ Economic status of occupants, demographics etc.
Explanatory variables identified from dataset‣ Number of HHLD occupants (cont.)
‣ HHLD income (cont.)
‣ Floor area (cont.)
‣ SAP (Standard Assessment Procedure) (cont.)
‣ Temperature difference (External - Internal) (cont.)
‣ Energy pattern (0-5 ) (categorical)
‣ Dwelling energy expenditure (and consumption) (cont.)
DATA
‣ Age of head of HHLD (cat.)
‣ Heating Degree Days (cont.)
‣ Urban dummy
‣ Owner dummy
‣ Economic status dummy
Standard Assessment Procedure
‣ SAP is the governments standard assessment procedure
for rating the energy performance of buildings.
‣ The adopted methodology in L1A and L1B (existing)
‣Measured on scale from 0 - 120
‣Multiple evolutions of procedure - 1996, 1998, 2001,
2005, 2008
Factors used to calculate SAP‣ Materials used for construction
‣ Thermal insulation of building fabric
‣ Ventilation characteristics of the dwelling and equipment
‣ Efficiency and control of the heating system
‣ Solar gain through windows and openings
‣ The type of fuel used to provide heating
‣ Any renewable energy technologies installed.
SAP
Descriptive StatisticsTable 1: Descriptive statistics for model variables
1. Std.Error and Std.Deviation calculations were c alculated from the re-calibrated effective sample s ize of (n c = 1025).
1.570.053.9561Age of head of household (categorical)
0.450.020.571.000.00Economic Status (dummy)
0.460.020.691.000.00Owner of house (dummy)
0.390.010.811.000.00Urban Recode (dummy)
2006.25208923671749Degree Days (categorical)
1.240.043.155.01.0Energy Pattern (categorical)
5.020.1612.127.1-20.0Temperature Difference (°C)
4.750.156.9039.1-9.2Outside temperature (°C)
2.790.0919.036.90.3Living room temperature (°C)
2848.87642333274.2Annual Energy Expenditure (£)
15.60.4944.41090SAP Rating
10,07231515,317103,8252,340HHLD Income (£)
31.20.9781.3252.420.0Floor Area (m2)
1.350.042.5110.001.00Number in household
Std. Deviation1Std. Error1MeanMaximumMinimum
OutlineMotivation
Context
Review of literature (modeling residential energy)
Data-sources and variables
Simple bivariate regression (the first case)
Overview of Structural Equation Modelling (SEM)
Methodology and application of SEM
Results
Policy implications
Bivariate regression
Table 2: bivariate regression results
Do homes that are more energy efficient consume less energy?
OutlineMotivation
Context
Review of literature (modeling residential energy)
Data-sources and variables
Simple bivariate regression (the first case)
Overview of Structural Equation Modelling (SEM)
Methodology and application of SEM
Results
Policy implications
Structural Equation Modelling (SEM)
Also known as:
� Causal modelling, path analysis, simultaneous equation modelling, LISREL
(linear structural relations), Covariance Structure Analysis.
SEM is an umbrella for three processes
1. Path analysis (uses measured variables)
2. Confirmatory Factor Analysis (latent variables)
3. Structural Regression Models (combination of 1 & 2)
The SEM ProcessSEM is an extension of the GLM
SEM techniques require LARGE datasets
“Normal” methods:
‣ Start with data and then use statistics to explain data (mean, standard deviation, covariance)
SEM methods use confirmatory technique
‣ Build model on previous knowledge and theory
‣ Test theory with underlying data
Many statistical methods are shown to be special cases of SEM
‣ Correlation, T-Test, ANOVA
‣ Two way ANOVA, Regression, MANOVA, CFA etc.
Theory
Results & Fit Indices
Interpretation
Model Construction
Data Preparation
Model Estimation
SEM Vocabulary
Latent Variable (Y,X)‣A variable in the model that is not directly measur ed. i.e. intelligence, democracy
Exogenous Variable (X)‣Variable that is not caused by another variable
‣Usually causes one or more variables in the model
Endogenous Variable (Y)‣Variable that is caused by one ore more variables i n the model.
‣May cause another endogenous variable.
Disturbance (error, residual) (ε)‣Unspecified causes of endogenous variables.
Symbols used in SEM
Manifest variable (indicator variable)
Unobserved or latent variables
Presumed direct causal effect
Presumed non-recursive (reciprocal) causal effect
(A causes B but B also causes A)
Covariance / Correlation between pair of exogenous
variables
ε Measured error in observed variable
Typical SEM layout
YY
XX11
XX22
XX33
DD
STANDARD REGRESSION MODEL
OutlineMotivation
Context
Review of literature (modeling residential energy)
Data-sources and variables
Simple bivariate regression (the first case)
Overview of Structural Equation Modelling (SEM)
Methodology and application of SEM
Results
Policy implications
Data preparation
Outliers -> Type I and and Type II errors.
‣ Univariate outliers
‣Multivariate outliers - Cook’s distance & centred leverage
‣ HHLD Income, Floor Area, Energy Expenditure, truncated to 5 std. from mean.
Missing Data
‣ Problematic in SEM if not handled correctly (Lee, 2005)
‣ Less than 5% missingness.
‣MNAR, MAR & MCAR. (Rubin, 1976)
‣ Listwise deletion, pairwise deletion, mean substitution, regression based
imputation, pattern matching & expectation maximisation.
‣ Tested effect of missingness in data -> EM Method.
Data preparation
Grossing weights -> minority groups over represented (social)
Large groups under represented (owners)
Original data is bias towards under-represented groups
Assumption of normality
Normality transformations showed that original data was
robust to small deviations from normality.
(((( ))))2
1
2
1
1,025
n
ii
c n
ii
wn
w
====
====
= == == == =∑∑∑∑
∑∑∑∑. ( )
c
Ss e x
n==== (Dorofeev, 2006)
Model specification
1 1 1 1 1
2 2 4 2 2 21
3 3 6 3 8 32
4 4 12 4 4
5 5 5 9 10 11 5 3 7 5
0 0 0 0 0 0
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0 0
0
y A y w
y A w y wx
y A w y wx
y A w y
y A w w w w y w w
εεεεε
= + + += + + += + + += + + +
Importing data into AMOS 18.0
Pearson Correlation
Energy
Expenditure
HHLD
Income
Floor
Area
Number of
Occupants
Temperature
Difference SAP
Energy
Pattern
Degree
Days
Energy Expenditure 1 0.375** 0.420** 0.452** 0.085** 0.031 0.188** 0.012
HHLD Income 0.375** 1 0.436** 0.475** 0.104** 0.110** 0.100** 0.013
Floor Area 0.420** 0.436** 1 0.352** 0.021 0.106** 0.109** -0.004
Number of Occupants 0.452** 0.475** 0.352** 1 0.053 0.104** .131** 0.004
Temperature Difference 0.085** 0.104** 0.021 0.053 1 0.034 .093** 0.123**
SAP 0.031 0.110** 0.106** .104** 0.034 1 .084** 0.012
Energy Pattern 0.188** 0.100** 0.109** 0.131** 0.093** 0.084** 1 0.04
Degree Days 0.012 0.013 -0.004 0.004 0.123** 0.012 0.04 1
**. Correlation i s s igni ficant at the 0.01 level (2-ta i led).
*. Correlation i s s igni ficant at the 0.05 level (2-ta i led).
Table 3: Correlation matrix for model variables
OutlineMotivation
Context
Review of literature (modeling residential energy)
Data-sources and variables
Simple bivariate regression (the first case)
Overview of Structural Equation Modelling (SEM)
Methodology and application of SEM
Results
Policy implications
ResultsTable 4: standardised direct effects
Model Results
Annual Energy Expenditure
0.31**
e4
Temperature Difference
0.19**
0.15**
0.27**
0.33**
0.09** 0.12**
0.05
0.09**
0.23**-0.22**
0.29
-0.050.23
0.02
0.11
e5
e1
0.27**
0.38**
0.19**
0.31**Number of Occupants
Floor Area
0.15**
0.38**
0.13**
e3e2
0.12**Household Income
Energy Pattern
0.23**-0.22**
SAP
0.13**
Explaining SAP
High propensity to
consume energySAP Rating
Low propensity to
consume energy
Measured energy
consumption
Floor Area
HHLD income
Number of occupants
Energy pattern
High energy
Low energy
Actual energy
consumption
Indirect & total effects
Table 5: standardised indirect effects
Table 6: standardised total effects
BootstrappingTable 7: Bootstrapping results
Model fit statisticsModel fit statistics in SEM are still widely debated
In SEM, the null-hypothesis (H0) is that the model is correct.
The alternative (Ha) is that it is not.
Therefore (and p-value) measures probability that model
fits perfectly to the population.
If P<0.05 we can’t reject null-hypothesis that the model is
correct and therefore have evidence the model may explain
reality.
2χ
Table 8: Model fit statistics
Results
£27.70Bedroom heated weekEnergy pattern
-£185.0030 -> 80 SAPSAP
Energy pattern
Temperature
Floor area
Number of occupants
HHLD income
Variable
£27.70Living room heated week
£2.50Each 1°C increase
£23.44Each extra 10m2
£88.32each extra person
£67.80Increase £10,000
Annual HHLD Energy
Expenditure
Effect
Table 9 : Total real effects on energy expenditure (1996)
Average income = £15,317 Average SAP rating = 44.4
Average occupancy = 2.51 Average annual energy expenditure = £642(1996) £1167(2009)
OutlineMotivation
Context
Review of literature (modeling residential energy)
Data-sources and variables
Simple bivariate regression (the first case)
Overview of Structural Equation Modelling (SEM)
Methodology and application of SEM
Results
Policy implications
Policy implicationsHomes with a propensity to consume more energy are shown to have relatively higher SAP rates.
� The scope for further savings from these homes may be limited.
� Homes with relatively high SAP ratings are subject to the law of diminishing returns.
Homes with a propensity to consume less energy, have lower SAP rates and therefore have greater potential to benefit from energy efficiency measures.
� These homes already consume relatively less energy.
� These homes are also more likely to be affected by the rebound effect.
This suggests an Energy Efficiency Barrier that must first be overcome.
This calls for more comprehensive and larger energy efficiency measures.
Different strategies for different energy consumers. Dual policy approach.
Summary
Serious lack of high quality, high resolution data for modellingresidential energy demand at the disaggregated level.
Limited number of disaggregated (bottom-up) statistical models of residential energy consumption.
Using SEM it is possible to isolate both direct and indirect effects to explain residential energy consumption.
At face value homes with high SAP rates do not automatically lead to low energy consumption.
If other factors are held constant (ceteris paribus) then increased energy efficiency does lead to lower energy consumption.
Do homes that are more energy efficient consume less energy?
ThanksScott Kelly
sjk64@cam.ac.uk
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Key Facts
Average HHLD energy demand is 22 MWh / year
Every 1 degree increase in heating season temp. leads
to a reduction of 1MWh / year
Energy price elasticity is measured at -0.2 this means a
50% increase in energy prices leads to 10% reduction
in energy demand.
(A. J. Summefield et al, 2010)
Non-recursivityStationarity assumption
‣ Requires the causal structure of the model not to c hange substantially over time.
‣ e.g. large houses will consume more energy.
Equilibrium assumption
‣ Any changes underlying the feedback relationship ha ve already manifested and come to equilibrium.
‣ e.g. high income HHLD’s effect on energy.