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Data-driven residential solar power forecasting
Haiwang Zhong, Associate ProfessorDepartment of Electrical Engineering
Tsinghua University
The Significance of Solar Power Forecasting
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装机
容量
/ 亿
千瓦
年份
Now:Wind 169GWSolar 78GW
2030: (High Penetration Scenario)Wind 1200GW ; Solar 1000GW
Year
Insta
llatio
n C
apa
city /
100
GW
2050: (High Penetration Scenario)Wind 2400GW ; Solar 2700GW
Wind PV&CSP
Developing distributed energy resources (DERs) is a global consensus.
Present
power grid
Smart Grid
2050
The Significance of Solar Power Forecasting
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Solar energy is one of the most promising and fast-growing renewable energy resources, which has
been widely deployed in China.
With the increasing penetration of solar energy, the intermittent and volatile nature of the weather-
based solar power poses significant challenges to the reliable and economic operations of the power
grid.
Clear sunny day Cloudy day
130GW
The Significance of Solar Power Forecasting
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Challenge 1: Reliability
High penetration of solar resources makes power flow approach the power limit of
substations in distribution grids.
Solar capacity: 1149.2
MW by 2016
Added capacity: over
600 MW in 2017
Policies for managing
rooftop solar into grids
Jiaxing, Zhejiang Province Poverty relief, Henan Province
Solar capacity: 540
MW by 2017
Added capacity: over
1000 MW during “13th
Five-Year Plan”
The Significance of Solar Power Forecasting
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Challenge 2: Flexibility
High penetration of solar resources dramatically raises the requirements for
flexibility in power grids.
Ramping requirement is getting larger. 5000 MW ramping
capacity from 17:00
to 18:00
The Significance of Solar Power Forecasting
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Challenge 3: Sustainability
A large amount of solar energy is curtailed and the utilization rate is low.
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30
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2014 2015 2016 2017
Inst
all
ed c
ap
aci
ty o
f
dis
trib
ute
d s
ola
r (G
W)
Year
820
830
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920
2014 2015 2016 2017
Uti
liza
tion
ho
ur
(h)
Year
China is experiencing a rapid development of distributed solar resources.
However, the utilization hour is low.
The Significance of Solar Power Forecasting
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Reliability
Flexibility
Sustainability
Other challenges: Power quality, voltage stability, harmonics, reactive power…
The accuracy of solar power forecasting is low:
1) Sunny days: RMSPE 8%
2) Other conditions: RMSPE 20%
Underlying cause
W. Glassley, J. Kleissl, H. Shiu, et al., “Current state of the art in solar forecasting, final report,”
California Institute for Energy and Environment, 2010.
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The methods of traditional solar power forecasting Solar power forecasting has been widely investigated in the past decades.
Forecasting methods can be classified from different aspects.
- Time: 1) minute/hour-ahead 2) day-ahead 3) week-ahead or longer
- Space: 1) solar array 2) solar station 3) distribution grid or larger
- Modeling: 1) analytical modeling 2) statistical methods
Statistical methods
Sun ray Wind
Cloud Temperature
Pressure Humidity Machine learning
20 40 60 80 100 120 140 160
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
So
lar
pow
er(
kW
)
Time(h)
Actual power
Forecasted power
20 40 60 80 100 120 140 160
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Sola
r pow
er(
kW
)
Time(h)
Actual power
Forecasted power
Forecast
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physical methodIt contains the physical relationship between weather and solar power, but the
modeling process is complex and it is difficult to simulate the nonlinear
relationship caused by some abnormal weather.
statistic methodThe intelligent algorithm has adaptive learning ability, but ignores the
analytical law contained in the physical method.
physical
method
statistic
method
High precision forecasting method combining physical
method and statistic method
The framework of solar power forecasting
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High precision forecasting method combining physical method and statistic
method
The framework of solar power forecasting
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The framework of solar power forecasting
Irradiance Wind
Rain Temperature
Weather data from numerical
weather prediction (NWP)
Spatial granularity: 3 km×3 km
Weather features: 179
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Datasets
Weather
Solar power
Weather
Analytical model
Solar irradiance
Cell temperature
PCA
KNN
SVM
Extract critical features
Find similar weather
Train and forecast
Statistical method
Solar power
Historical data
Forecast process
The framework of the proposed approach
Historical datasets
- Weather conditions
- Solar power
Analytical model
- Calculate different
components of irradiance
- Calculate PV cell temperature
- Generate critical weather
features
Statistical method
- Use critical weather features
- PCA→KNN→SVM
The framework of solar power forecasting
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Case study
To verify the accuracy and effectiveness of the proposed forecasting approach, real-
world datasets from PV systems in Australia are used. The following error indices are
adopted to measure the forecast accuracy.
(1) Normalized mean absolute error (nMAE)
(2) Normalized root mean square error (nRMSE)
(3) Normalized largest absolute error (nLAE)
(4) Energy production error (EPE)
2
12
ˆ1
nMAE 100%H
t C
t
H
t
P
*
mp mpP P
2
2
1 2
ˆ1
nRMSE 100%H
tC
t
P
t
H
*
mp mpP P
1 ˆnLAE max , 100%C
tP
t t *
mp mpP P
2
2
1
1
ˆ
EPE 100%
H
t
H
t
t t
t
*
mp mp
mp
P P
P
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Case study
To demonstrate the effectiveness of the analytical modeling, different forecasting
methods with and without analytical modeling are compared in the case studies.
Acronym Forecasting engine Analytical modeling
S1 SVM √
S2 SVM ×
A1 ANN √
A2 ANN ×
W1 Weighted KNN √
W2 Weighted KNN ×
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Case study
Data description
Zone Latitude Longitude Capacity Array azimuth Array tilt
1 35°16’30’’S 149°6’49’’E 1.56 kW 38° 36°
2 35°23’32’’S 149°4’1’’E 4.94 kW 327° 35°
3 35°32’S 149°9’E 4.00 kW 31° 21°
Scenario Forecasting dataset
Spring From October 1, 1:00, 2013 to October 31, 24:00, 2013
Summer From January 1, 1:00, 2014 to January 31, 24:00, 2014
Autumn From April 1, 1:00, 2014 to April 30, 24:00, 2014
Winter From July 1, 1:00, 2013 to July 31, 24:00, 2013
Sunny 30 days with highest GHI from July 1, 2013 to May 1, 2014
Cloudy 30 days with highest cloud coverage from July 1, 2013 to May 1, 2014
Humid 30 days with highest relative humidity from July 1, 2013 to May 1, 2014
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Case study
Top 10 dominant key weather factors
Global horizontal irradiance
Diffuse horizontal irradiance
Diffuse horizontal irradiance
and relative humidity
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Case study
Forecast results in four seasons
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Case study
Forecast results in three weather conditions
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Case study
Error indices in typical seasons
No. ApproachnMAE nRMSE
Winter Summer Winter Summer
1
S1 5.11% 2.51% 8.17% 4.58%
S2 6.06% 5.14% 9.03% 7.25%
2
S1 1.66% 1.53% 2.56% 2.88%
S2 1.77% 2.63% 2.58% 3.78%
3
S1 1.83% 1.17% 2.89% 1.97%
S2 2.35% 1.88% 3.31% 2.63%
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Publication
IEEE Transactions On Smart Grid
