This work presents a data-intensive solution to predict Photovoltaïque energy (PV) production. PV and other renewable sources have widely spread in recent years. Although those sources provide an environmentally-friendly solution, their integration is a real challenge in terms of power management as it depends on meteorological conditions. The ability to predict those variable sources considering meteorological uncertainty plays a key role in the management of the energy supply needs and reserves. This paper presents an easy-to-use methodology to predict PV production using time series analyses and sampling algorithms. The aim is to provide a forecasting model to set the day-ahead grid electricity need. This information is useful for power dispatching plans and grid charge control. The main novelties of our approach is to provide an easy implemented and flexible solution that combines classification algorithms to predict the PV plant efficiency considering weather conditions and nonlinear regression to predict weather forecasted errors in order to improve prediction results. The results are based on the data collected in the Techno-pôle’s microgrid in Sierre (Switzerland) described further in the paper. The best experimental results have been obtained using hourly historical weather measures (radiation and temperature) and PV production as training inputs and weather forecasted parameters as prediction inputs. Considering a 10 month dataset and despite the presence of 17 missing days, we achieve a Percentage Mean Absolute Deviation (PMAD) of 20% in August and 21% in September. Better results can be obtained with a larger dataset but as more historical data were not available, other months have not been tested.

1. Solar production prediction based on
non linear meteo source adaptation
Mariam Barque, IMIS 07-2015, Blumenau

2. Content
 Interest of the study
 Aim of the study
 Test bed
 Methodology
 Main results
 Conclusion

3.  Interest of the study
 Goal of the study
 Test bed
 Methodology
 Main results
 Conclusion
Content

4. Who wants to predict solar energy
and why ?
Interest of the study

5. Content
 Interest of the study
 Aim of the study
 Test bed
 Methodology
 Main results
 Conclusion

6. What we want
 A day ahead solar production prediction
 Operational
 Easy-understanding
Aim of the study
Weather information
Historical production
Solar production predictionPREDICTION
0
50
100
150
200
0 2 4 6 8 10 12 14 16 18 20 22
kWh
Hour
J+1

7. Content
 Interest of the study
 Aim of the study
 Test bed
 Methodology
 Main results
 Conclusion

8. Techno-Pôle of Sierre
Test - bed
Transformer measure
Consumption measure
PV Production measure
Batterie 25KWh
Weather station

9. What we have
Oct 2013-Oct
2014
Past Weather
measures
Temperature
Radiation
PV production
per second
Power
production (kW)
Forecasted
Weather
measures
Temperature
Radiation
Test - bed
 One year of power production measures
 Weather data for Sion (~7 km from the testbed)
 Prediction for 20% of the data set (August and September)

10. Content
 Interest of the study
 Aim of the study
 Test bed
 Methodology
 Main results
 Conclusion

11. Electricity production mechanism
 Assume
Methodology
𝑃𝑊 = 𝑅𝑎𝑑 × 𝛼
𝑃𝑊
𝑅𝑎𝑑
90°

12. Global process
Methodology
DATA PRE -
PROCESSING
NIGHT/DAY
SPLITTER
ALPHA
PREDICTION
WEATHER
PREDICTION
CORRECTION
POWER
PREDICTION
-Time conversion
- Hourly
aggregation
-Alpha calculation
-Based on daily
sunrise and sunset
data
- Clustering
- Decision tree
𝛼 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑
- Radiation re-
estimation
- Polynomial
regression
(𝑅𝑎𝑑 𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑑)
- Power
calculation
- Day/night
concatenation
𝑃𝑊𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑
=
𝑅𝑎𝑑 𝑐𝑜𝑟𝑟𝑒𝑐𝑡 × 𝛼 𝑝𝑟𝑒𝑑𝑖𝑐𝑡
0
50
100
150
200
0 2 4 6 8 10 12 14 16 18 20 22
kWh
Hour
Production on 27/08/2014

13. Alpha prediction
Methodology
Learning (80% of the dataset)
EM
Clustering
Decision
tree
Learner
Clusters
T° real
Hour
Rad
alpha
T° real
Hour
Prediction (20% of the dataset)
Decision
tree
Predictor
Clusters
predicted
Alpha
estimation
(mean of the
cluster)
Alpha
predicted
Hour
T° real
Rad

14. Clustering step
 Number of cluster optimized : 6
Methodology
EM
Clustering
Clusters
alpha
T° real
Hour

15. Decision tree step
 Classification step
 Accuracy of 88%
Methodology
Decision
tree
Learner
T° real
Hour
Rad
Decision
tree
Predictor
Clusters predicted
Hour
T° real
Rad
Clusters

16. Methodology

17. Alpha predicted value
Cluster Mean
0 0.22
1 0.13
2 0.2
3 0.18
4 0.17
5 0.2
Methodology
Clusters
predicted
Alpha
estimation
(mean of the
cluster)
Alpha
predicted

18. Global process
Methodology
DATA PRE -
PROCESSING
NIGHT/DAY
SPLITTER
ALPHA
PREDICTION
WEATHER
PREDICTION
CORRECTION
POWER
PREDICTION

19. Weather prediction correction
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200
400
600
800
1000
0 2 4 6 8 10 12 14 16 18 20 22
kW
Hour
Corrected Radiation on 06/07/2014
Corrected radition Predicted radiation
Real radiation
Methodology
𝑃𝑊𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑 = 𝑅𝑎𝑑 𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑒𝑑 × 𝛼 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑
0
100
200
300
400
500
600
700
800
900
1000
06.09.2014
06.09.201406:00
06.09.201412:00
06.09.201418:00
07.09.2014
07.09.201406:00
07.09.201412:00
07.09.201418:00
08.09.2014
08.09.201406:00
08.09.201412:00
08.09.201418:00
09.09.2014
09.09.201406:00
09.09.201412:00
09.09.201418:00
10.09.2014
10.09.201406:00
10.09.201412:00
10.09.201418:00
11.09.2014
11.09.201406:00
11.09.201412:00
11.09.201418:00
W/m2
Date
Sum of gre000b0 Sum of GLOB
 16% to 5% of errors on the test set

20. Global process
Methodology
DATA PRE -
PROCESSING
NIGHT/DAY
SPLITTER
ALPHA
PREDICTION
WEATHER
PREDICTION
CORRECTION
POWER
PREDICTION
𝑷𝑾 𝒑𝒓𝒆𝒅𝒊𝒄𝒕𝒆𝒅 = 𝑹𝒂𝒅 𝒄𝒐𝒓𝒓𝒆𝒄𝒕𝒆𝒅 × 𝜶 𝒑𝒓𝒆𝒅𝒊𝒄𝒕𝒆𝒅

21. Content
 Interest of the study
 Aim of the study
 Test bed
 Methodology
 Main results
 Conclusion

22. Results
August September Average
A: Full methodology 20% 21% 20%
B: Perfect radiation
forecast
14% 18% 16%
C: Without weather
prediction
correction
24% 28% 26%
Main results
 3 Scenarios
 A: Full methodology results
 B: Results assuming perfect radiation forecast
 C: Results without weather prediction correction
𝑃𝑊𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑 = 𝑅𝑎𝑑 𝑟𝑒𝑎𝑙 × 𝛼 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑
𝑃𝑊𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑 = 𝑅𝑎𝑑 𝑓𝑜𝑟𝑒𝑐𝑎𝑠𝑡 × 𝛼 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑
 20% overall error
 16% of error on alpha coeffiscient forecast
 6% of error saved with the weather prediction correction

23. Accurate prediction example
Main results
 Error of 5%
 Shiny day
 Accurate weather
forecast data0
20
40
60
80
100
120
140
160
180
0 2 4 6 8 10 12 14 16 18 20 22
kW
Hour
19.08.2014
Real power Predicted power

24. High error prediction example
Main results
 Error of 40%
 Error due to
Radiation prediction
error (48%)
 Highest errors occurs
between 11AM to
2PM
0
50
100
150
200
250
300
350
400
450
0 2 4 6 8 10 12 14 16 18 20 22
kW
Hour
20.01.2014
Real power
Predicted Power
Predicted radiation

25. Content
 Interest of the study
 Aim of the study
 Test bed
 Methodology
 Main results
 Conclusion

26.  First approach applicable with limited parameters
20% error on the test set
 Only one year data, 20% of the year is predicted
 Improving the prediction
 Alpha prediction step with other algorithms
 More weather parameters
 Application example
Conclusion

27. Thank you for your attention,
QUESTIONS ? ..
Authors: Mariam Barque, Luc Dufour, Dominique Genoud, Bruno Ladevie, Jean- Jacques
Bezian, Arnaud Zufferey