The 25thInternational Workshop

Assessing climate sensitivity ofhourly electricity demand in Japan
Yuki HIRUTA, Lu GAO and Shuichi ASHINA

November 18-19, 2019

National Institute for Environmental Studies
Center for Social and Environmental Systems Research
Research Associate

Yuki Hiruta

■Motivations

International Workshop

Global warming• Highly fluctuating electricity demand•Decrease the efficiency of electricity supply system•Increase the fossil fuel consumption• Warming urban environment in summer

Urbanization•Multiple problems such as heat strokes

It is important to clarify the relationshipbetween weather conditions
and the hourly electricity demand

■Overview
Target Areas• Jurisdiction of 10 electric power companies in Japan(EPC)
Method over view

Build regression models for each EPC
Predictor

Multiple variables

Related to ••weather conditions
daily cycle of human activities

Explained variable

Hourlyelectricity consumption

Simulation
Represent the Temperature response functions
Based on simulation by the constructed models
Understanding the relationshipbetween weather conditions and the hourly electricity demand

■Temperature response function(TRFs)*Also called Energy signature

Base Demand
Comfort Zone
Temperature( )V-shaped
Electricity consumption

Electricity consumption

U-shaped

BPT:

Base Demand

Balance pointtemperature
BPT(Balancedpoint temperature)
Temperature( )Application:

TRFs → Basic Units• BPT → Reference temperature for HDD/CDD

Important!
Affects
assumptions ofother models!

• BPT, Slope → Electricity demand projection
4■Data and Variables
Names （Abbreviations）
Explained
Variable
Predictors

Historical
weather

Thermalindex

Historical electricity demand（EC） MWh
Temperature（TEMP）℃

Humidity（HUM）%

Solar radiation（SUN）

MJ/m

Wind speed（WIND）

m/s

Rainfall amount（RAIN）

mm

Snow depth （SNOW）

cm

Discomfort Index（DI）­Wind Chill（WCI）­Holidays and weekends dummy（HDD）
HumanactivityUnits

Working people（WORK%）
Awake people（WAKE%）
Sleeping people（SLEEP%）­%%%

Data Sauce

Data Description
Hourly electricity demand in each EPC
jurisdictional area.
Hourly averaged air temperature in FY2016
and FY2017
Hourly averaged relative humidity in FY2016
and FY2017
Hourly averaged total radiation in FY2016
and FY2017
Average wind speed in ten minutes before
each hour in FY2016 and FY2017
Hourly total rainfall amount in FY2016 andFY2017Hourly total snow depth in FY2016 andFY2017
Discomfort Index.
DI0.81 TEMP

0.01 HUMWind Chill Index.
WCI33

TEMP 10.45

0.99 TEMP

10 WIND .

14.3

46.3

WIND

Weekends, holidays, the New Year, and theObon Festival were set as 1, and all otherdays were set as 0 in FY2016 and FY2017

Organization for
Cross-regional
Coordination ofTransmission
Operators Japan,

Japan
MeteorologicalAgency

Derived fromHistorical
Weather Data
Above
Calendars inFY2016 andFY2017

The percentage of people who are working atthe hour.
NHK Broadcasting
The percentage of people awake in theirCulture Researchhomes at the hour.
Institute.
The percentage of people who are sleeping atthe hour.

5■Human activity data%

Human activity in each hour

1009080706050403020
100161218(Data source: NHK Broadcasting Culture Research Institute, 2015)

24 Hour

6

■Model construction
MARS：multivariate adaptive regression splines (Friedman, 1991)•Captures the complexity of the potential model by applying a locallylinear models.

•Selects important variables during the model building process.

•Showed excellent prediction performance in short-term power
consumption modeling (Sigauke & Chikobvu, 2010; Al-Musaylh, Deo, Adamowski, & Li, 2018)
Intuitive understanding for MARS performance• Can model nonlinearities inFlexible
and Highly generalizablehigh-dimensional
data:flexible
modeling is expected• Highly generalizable

7

■Constructed models
Performed well in all power company models.
0.870(Kyushu ) 〜 0.953(Okinawa)
Hokkaido Tohoku

TokyoChubu

Hokuriku

Kansai

Chugoku Shikoku

Kyushu Okinawa

R

0.922

0.909

0.935

0.902

0.890

0.933

0.908

0.903

0.873

0.953

generalized R

0.921

0.907

0.934

0.900

0.887

0.932

0.907

0.901

0.870

0.953

Number of terms

353929373925

29323530

Number of predictors adopted

99810977789

Number of input predictors

11111111111111111111

Adoptedpredictors

TEMP〇〇〇〇〇〇〇〇〇〇

HUM----------

SUN

〇〇〇〇〇

〇

〇

〇〇

〇

WIND---〇〇---〇〇RAIN〇-〇〇-----〇

SNOW〇〇--〇-----DI

〇

〇

〇

〇〇

〇

〇

〇〇

〇

WCI-〇-〇-----〇HDD〇〇〇〇〇〇〇〇〇-WORK%〇〇〇〇〇〇〇〇〇〇

WAKE%〇〇〇〇〇〇〇〇〇〇SLEEP%〇〇〇〇〇〇〇〇〇〇8

■Model performancex-axis：Actual （MWh）y-axis：Prediction（MWh）

High-quality models in terms of〇 ：In-sample result＋ ：Out-of-sample result

both fitting and generalization：OLS regression line for in-sample result：OLS regression line for out-of-sample result：The coefficient of determination of OLS (in-sample)：The coefficient of determination of OLS (out-of-sample)

Hokkaido

5,500Tohoku

Tokyo

14,000

5,000
4,500

Chubu

Hokuriku

25,0005,000

50,00012,00020,0004,000

40,0004,000
10,00015,000

3,500
3,000

8,000

0.922
0.909

2,500

Kansai

6,000

11,00025,000

0.909
0.888

6,000

2,500 3,000 3,500 4,000 4,500 5,000 5,500

3,000

30,0008,000

10,00012,000

0.935
0.934

20,00020,00014,000Chugoku

30,00040,00050,000Shikoku

4,000

8,000
7,000

3,000

15,000

0.933
0.922

10,00010,00015,00020,00025,000

6,000
5,000

0.908
0.917
5,000 6,000 7,000 8,000 9,000 10,00011,000

15,00020,000

2,000
2,000

3,000

4,000

2,000

Kyushu1,2,0010,000

1,0,005,000

Observed values vs estimated values

4,000

5,000

Okinawa

12,000

6,000

3,000

1,400

8,000

0.903
0.896

0.890
0.895

2,000

25,00014,0009,000
20,00010,00016,0005,000

10,000

0.902
0.882

10,0000.873
0.896
6,000 8,000 10,000 12,000 14,000 16,000

800

0.953
0.960

600
600

800

1,0,00

1,2,001,400

9Simulating
Temperature Response Functions

10■Hourly simulation
Settings for the simulation
Weather predictors

x-axis：Temperature（ ）Regular sequences of the value

TEMPy-axis：Power consumption（MWh）

Average values of each time period at eachlocation in weekdays.

Weather predictors
HUM, SUN, WIND, RAIN, SNOW

The thermal indicators〇

Calculated from the weather predictors .

DI, WCI

Holiday dummy

Hourly simulation

Weekdays: 0HDD

Human activity predictors

HourValues of each time period in weekdays.

WORK%, WAKE%, SLEEP%

Hokkaido

Tohoku

Chubu

Tokyo

55,000

Hokuriku

25,00014,0005,000

Observation

5,000

50,000

4,500

4,500

45,00012,00020,0004,000

40,0004,000
10,0003,5003,500

35,00015,0003,000

30,000
3,000

8,000

2,500

25,0002,500

10,0002,000

20,000
100

1020

30

100

Kansai

1020

30

100

Chugoku

25,00020,000

1020

30

100

Shikoku

10,000

5,000

9,000

4,500

100

102030Okinawa
1,400

1,2,0012,0003,000

15,00030

14,000

3,500

7,000

20

Kyushu

4,000

8,000

10

6,000

10,000

1,0,00

8,000

800

2,500
10,000

5,000
100

10

20

30

100

10

20

30

2,000

6,000
100

10

20

30

600
100

1020

30

100

1020

30

11■Simulation in day-time and night-time
Settings for the simulation
Weather predictors

x-axis：Temperature（ ）Regular sequences of the value

TEMP

y-axis：Power consumption（MWh）

Average values in day‐time and night‐time ateach location in weekdays.

Weather predictors
HUM, SUN, WIND, RAIN, SNOW

The thermal indicators〇

Calculated from the weather predictors .

DI, WCI

Holiday dummy
HDD

Human activity predictors
WORK%, WAKE%, SLEEP%

Weekdays: 0

Day-time simulation(from 10:00 to 18:00)

Average values during day‐time and night‐timein weekdays.

Night-time simulation(from 1:00 to 5:00)

HokkaidoTohokuTokyo55,000

Chubu

Hokuriku

25,00014,0005,000

Observation

5,000

50,000

4,500

4,500

45,00012,00020,0004,000

40,0004,000
10,000

3,500

3,500

35,00015,0003,000

30,000
3,000

8,000

2,500

25,0002,500

10,0002,000

20,000
100

10

20

30

100

Kansai

1020

30

100

Chugoku

25,00020,000

1020

30

100

Shikoku

10,000

5,000

9,000

4,500

100

10

20

30

Okinawa
1,400

1,2,0012,0003,000

15,00030

14,000

3,500

7,000

20

Kyushu

4,000

8,000

10

6,000

10,000

1,0,00

8,000

800

2,500
10,000

5,000
100

1020

30

100102030

2,000

6,000
100

102030600
100

10203010010203012■Approximation
x-axis：Temperature（ ）“Temperature response functions”

y-axis：Power consumption（MWh）〇

are approximated by piecewise

Day-time simulation
Night-time simulation

linear function using MARS.

Hokkaido

TohokuApproximate functionTokyo

55,000

Hokuriku
5,000

50,000

4,500

4,500

45,00012,00020,0004,000

40,0004,000
10,000

3,500

3,500

35,00015,0003,000

30,000
3,000

8,000

2,500

25,0002,500

10,0002,000

20,000100102030100Kansai

102030100

Chugoku

25,00020,000102030100

Shikoku

10,000

5,000

9,000

4,500

30

100

102030Okinawa
1,400

1,2,0012,0003,000

15,00020

14,000

3,500

7,000

10

Kyushu

4,000

8,000

breakpoints

Chubu
25,00014,0005,000

Observation

6,000

10,000

1,0,00

8,000

800

2,500
10,000

5,000
100

1020

30

100

1020

30

2,000

6,000
100

10

20

30

600
100

10

20

30

100

1020

30

13■Approximate function
Parameters(the coordinates of breakpoints,

x-axis：Temperature（ ）the coefficients of each linear function)

y-axis：Power consumption（MWh）

can be obtained
from the piecewise linear function〇 Observation
Approximate function for day-time
Approximate function for night-time

Provide the parameters to other models
Hokkaido

TohokuTokyo

55,000

Chubu

5,000

Hokuriku

25,00014,0005,000

50,000

4,500

4,500

45,00012,00020,0004,000

40,0004,000
10,000

3,500

3,500

35,00015,0003,000

30,000
3,000

8,000

2,500

25,0002,500

10,0002,000

20,000100102030100

Kansai

102030100

Chugoku

25,00020,000102030100

Shikoku

10,000

5,000

9,000

4,500

100102030Okinawa
1,400

1,2,0012,0003,000

15,00030

14,000

3,500

7,000

20

Kyushu

4,000

8,000

10

6,000

10,000

1,0,00

8,000

800

2,500
10,0005,000
100102030

1001020

30

2,000

6,000
100102030

600
100

10203010010203014■The effect of humidity on power consumption
Settings for the simulation

x-axis：Temperature（ ）Weather predictors

Regular sequences of the value

Weather predictors

SUN, WIND, RAIN, SNOW

Average values in day‐time at each location inweekdays.

The thermal indicators
DI, WCI

Calculated from the Weather predictors .

Holiday dummy
HDD

Weekdays: 0

Human activity predictors

Values for each time period in weekdays.

TEMP,HUM

WORK%, WAKE%, SLEEP%

Hokkaido

y-axis：Power consumption（MWh）〇

Relative humidity

Tohoku

1020

Tokyo

55,00030

40

50

10,0003,500

20,0003,500

35,0008,000

15,0003,000
2,500

25,0002,500

10,0002,000

20,000
100

1020

30

100Kansai

102030100

Chugoku

25,00020,000

1020

30

100

Shikoku

10,000

5,000

9,000

4,500

30

100

102030Okinawa
1,400

1,2,0012,0003,000

15,0002014,000

3,500

7,000

10Kyushu

4,000

8,000

%

100

4,000

30,000
3,000

90

5,000

40,000

4,000

80

4,500

45,00012,00070

Hokuriku

50,000

4,500

60

Chubu
25,00014,0005,000

Observation

6,000

10,000

1,0,00

8,000

800

2,500
10,000

5,000
100

1020

30

100

1020

30

2,000

6,000
100

1020

30

600
100

10203010010203015■The effects of humidity in detail
A simulation
for Tokyo in daytime
as an example

An actual humidity
range in Tokyo when
the temperature
is higher than 30℃
27%

80%

Temperature 30℃
Humidity 80%

Power consumption
reducedby 13.8%.
Temperature 30℃
Humidity 27%

16

■ BPT(Balance point temperature)
BPT(Balance point temperature)
The bottom of the V-shaped
temperature response function(Amato et al. 2005)%

BPT decrease as humidity rises:Under the conditions wherein the humidity
is high, the power consumption for cooling
begins to increase at lower temperatures.
17

■Summary
we proposed a series of methods to understand the relationshipbetween weather conditions and the hourly electricity demand
Summarized results• The constructed models are of high-quality in terms of1)fitting, 2)generalization capability, and 3) simulating accurate
temperature response functions•

Two different Temperature Response Functions were identified in a day;for day-time (from 10:00 to 18:00) and for night-time (from 1:00 to 5:00).

•Humidity affects electricity consumption significantly when temperature
is high•

Under the condition wherein the humidity is high, the power consumption
for cooling begins to increase at lower temperatures.

Suggestions• The proposed method is recommended for identifying temperature
response functions; especially the consideration of multiple factors
are important.
•The effect of humidity should not be ignored.

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