Energy Consumption Comparison Tool

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Energy Consumption Comparison Tool in R

Introduction

Some cities/energy companies have attempted to reduce energy consumption by

informing consumers how their energy consumption compares to their neighbours. This is a

method that Thaler and Sunstein (2008) call nudging. Through peer comparison and social norms,

the cities/companies hope to motivate people to find ways to reduce energy consumption. The

Energy Consumption Comparison tool can be used to compare a households energy

consumption with other households in the country and generate customized reports for the

household. After entering their own household characteristics, the user will receive a report

comparing their consumption with a variety of benchmarks. Regression equations are used to

calculate some of these benchmarks based on existing data.

Process

There are 3 major stages to the tools process. The first involves taking the data input and

rendering them in a suitable form for analysis. The second stage runs a geographically weighted

regression to determine the coefficients to use for predicting energy consumption levels for

different planning areas, and the third stage creates the report of the comparison analysis. Figure

1 depicts the tools process, which is explained in further detail below.

Stage A: Input Data

The tool requires 4 sets of data:

a. Population data. This consists of data on number of people by gender and age group as

well as the number of different types of flats (1 room to 5 room flats and executive

condos) in each of the 55 planning areas, which are further subdivided into 323 subzones.

The data used for the demonstration is a csv file consisting of data for 2011 to 2014 from

Singapores national statistical authority.

b. Electricity consumption data. This consists of data on average monthly electricity

consumption in each of Singapores 55 planning areas, in terms of kilowatt-hours. The

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consumption data can be categorized in terms of the different types of flats (1 room to 5

room flats and executive condos) in each planning area. The data used for the

demonstration is a csv file consisting of 2005 to 2013 electricity consumption data from

Singapores energy authority.

c. Planning area boundaries. This is a .shp file containing the boundaries of Singapores

55 planning areas. The file used for the demonstration is from Singapores energy

authority.

d. Household characteristics of the individual household. The user is asked to provide

information on their own household to the tool. This includes the planning area that they

live in, the type of flat they live in, and their electricity consumption for the month in

kilowatt-hours. In the demonstration, the user lives in a 4-room flat in Yishun (planning

area 4), and has consumed 375kwh of electricity last month.

Figure 1: Energy Consumption Comparison Tool Flow Chart

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Stage B: Geographically Weighted Regression

A geographically weighted regression is run on the population and electricity

consumption data using the following formula:

Energy Consumption ~ Population Above 65 + Number of 1 to 2 Rm Flats + Number of 3 Rm Flats

This model was previously chosen through regression analysis on 2013 population and energy

consumption data. Further discussion on model selection may be found below. The

geographically weighted regression analysis will generate a set of coefficients for each planning

area, which will be used to predict the 2014 energy consumption figures for each planning area

that will be used as one of the benchmarks for comparison.

Stage C: Output

The coefficients from the geographically weighted regression and the user-provided

household characteristics will be combined to provide 2 outputs for the user. Both are pdf files

that will be saved in the working directory. The first output is a summary table that will be saved

as summary.pdf. This compares the users household energy consumption against 4 different

benchmarks:

a. The national average for 2013;

b. The national average for the users type of flat;

c. The average for the users planning area (2014 estimate based on geographically

weighted regression); and

d. The average for the users planning area (2013 actual data).

Figure 2 provides a sample of the summary table for a user who lives in a 4 room flat in Yishun

(planning area 4), who has consumed 375kwh of electricity last month.

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Figure 2: Sample Summary Table

The second output is a map comparing the users electricity consumption with the average of

those living in the same type of flat in other planning areas. The user is doing worse than average

in red areas, and better than average in green areas. Figure 3 provides a sample comparison map

for the same Yishun user. Grey area are areas without flats.

Figure 3: Sample Comparison Map

Selection of Regression Model

Dennett (2014)s geographically weighted regression method was adapted to identify the

best model to use for the geographically weighted regression in the Energy Consumption

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Comparison Tool. The actual code used and the different models tested can be found in

Regression Analysis.R. The data used for the code is the same population and energy data as

the ones used for the Energy Consumption Comparison Tool. Table 1 provides a summary of the

results for the 7 models tested for 2013 data. The results for 2011 and 2012 were similar. Models

1 to 5 tested a number of factors in each planning area that may influence energy consumption in

households: population, number of flats, number of flats of each type, age, gender, and

population per flat. Models 6 and 7 combined factors that were significant from the first 5

models. Model 7 was chosen for the geographically weighted analysis as it had the lowest p-

value for its F-statistic. Even though model 6 has a higher adjusted R2 than model 7, it was

discarded as it contains factors that are insignificant. All factors in model 7 are significant both

globally and locally.

Table 1: Summary of Results for Regression Model Selection

Model p-value

for F-stat

Adjusted

R2

1. Energy Consumption ~ Population + No. of Flats 0.2475 0.0328

2. Energy Consumption ~ No. of 1 and 2-rm flats** + No. of 3-rm flats + No. of 4-rm flats + No. of 5-rm flats and exec condos

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To motivate users further, the tool could focus on areas where users are doing worse than

the benchmarks. Fischer(2008) found that comparing energy consumption with benchmarks may

result in zero energy consumption changes overall as the users who are below the benchmark

may feel that it is alright to increase energy usage and thus offset any cuts made by users above

the benchmark. More fine-grained data, such as the energy consumption data for individual flats

could be helpful for nudging users into action.

Beyond energy consumption, the tool could be adapted for use to other situations where

comparing data between planning areas using regression equations is useful. Other phenomenon

that could be analyzed in this way are water/gas consumption, car ownership or education level.

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References DENNETT, A. (2014). An Introduction to Geographically Weighted Regression in R. [Online]

Available from: http://rpubs.com/adam_dennett/44975. [Accessed: 31st Dec 2014].

FISCHER, C. (2008). Feedback on household electricity consumption: a tool for saving energy?

Energy Efficiency. 1(1). p.79-104.

THALER, R.H. & SUNSTEIN, C.R. (2008). Nudge: Improving Decisions about Health, Wealth

and Happiness. London: Penguin.

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User Documentation

Data Input

The Energy Consumption Comparison Tool makes use of the following files:

a. Population data (Population data panel.csv). The variables used by the regression

equation in the tool are under the columns PopAbove65, HDB1ro2RmFlats and

HDB3RmFlats. The column ID is used to merge the population data with the

electricity consumption data.

b. Electricity consumption data (EMA data panel.csv). The variable used by the

regression equation in the tool is under the column EOverall. The column ID is used

to merge the electricity consumption data with the population data. The column Year is

used to extract the data for the relevant year.

c. Planning area boundaries (EMA_PLANBDY_DWELLING_2013.shp). This is

used solely for the planning area boundaries.

Geographically-Weighted Regression

The formula for the regression equation can be changed in line 75 if needed. Any variables found

in the population data or electricity consumption data files can be used in the regression equation.

Displaying the results of alternate regression equations in the summary or on the map would

however require further changes in the script.

Output

The 2 output files are named ComparisonMap.pdf and Summary.pdf and can be found in the

working directory, which should be set in line 2.