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Andreas Livera1, Marios Theristis1, George Makrides1, Juergen Sutterlueti2, Steve Ransome3 and George E. Georghiou1
1PV Technology Laboratory, University of Cyprus, Nicosia, Cyprus2Gantner Instruments GmbH, Schruns, Austria3Steve Ransome Consulting Ltd, Kingston upon Thames, UK
Performance analysis of mechanistic and machine learning models forphotovoltaic energy yield prediction
Outline
2
• Introduction
• Motivation
• Methodology
• Results
• Conclusions
• Future Work
Introduction
3
• Accurate energy yield prediction is crucial for the performance assessment andmonitoring of PV systems
• Existing features of monitoring systems depend on electrical, empirical andmachine learning models to predict the energy yield of PV systems
Background & Objective
4
Model architecture construction conditions
• Input/output features• Irradiance filtering conditions• Duration of train subset• Irradiance profile classification
Specific Objective: Development of an optimized predictive model methodologybased entirely on the acquired measurements
Methodology – Approach
5
Yearly dataset
Train set
Test Set(30 %)
Determine accuracy
Model training
Produce modelTest model
Performance metrics
Experimental setup – Data acquisition system (DAQ)
• Test-bench PV system in Cyprus• Test-bench PV module in Arizona
Data quality routines (DQRs)• Data filtering (𝐺𝐼 > 0.1 𝑘𝑊/𝑚2)• Identify missing/erroneous values• Correction/Imputation of data
Train, test and improve the model• Train model• Evaluate performance
Irradiance: Plane of array Gi from pyranometers
Met data: Wind speed and direction, Relative Humidity, Tambient
PV data: MPP DC Current, Voltage and Power, Tmodule
60-min average measurements
Irradiance: Plane of array Gi from pyranometers, cSi and reference cellsHorizontal Gh, Dh, Beam normal Bn, spectral 350-1050nm
Met data: Wind speed and direction, Relative Humidity, Tambient
PV: Fixed and 2D track, IV curve every minute, Tmodule
60-min instantaneous measurements
UCY OTF - Nicosia, Cyprus
GI OTF - Arizona, USA
Methodology – Predictive model selection
6
Mechanistic Performance Model
Feed-Forward Neural Network (FFNN)
Machine Learning
[1] S. Theocharides, G. Makrides, A. Kyprianou, and G. E. Georghiou, “Machine Learning Algorithms for Photovoltaic System Power Output Prediction,” in 5th ENERGYCON, 2018
4 inputs, 8 coefficients, Gi from pyranometer3 inputs, 5 coefficients (C1-C5), Gi from reference cell*Gi from pyranometer → correct for AOI and Beam fraction
Results – Model fit robustness
7
ML – Lower SD, RMSE error and higher R
Good predictive quality using both instantaneous
and average measurements
Random 70:30 % - GI OTF Random 70:30 % - UCY OTF
MPM – Should have been corrected for AOI and
Beam fraction (UCY OTF)
Results – Model fit robustness
8
Coefficient Value
C1 (%) 118.66
C2 (%/K) -0.38
C3 (%) 31.46
C4 (%) -22.58
C5 (%/ms-1) 0.03
MPM – Useful, physically meaningful coefficients
MPM modelling can also be used for faults identification (i.e. underperformance) and for determining degradation rates
5CV.4.35
Results – Filtering
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ML – More accurate at low andmedium irradiance conditions
• Elimination of low, medium and high irradiance conditions (less than 0.1, 0.3 and 0.6 kW/m²)
MPM – More accurate at highirradiance conditions
Random 70:30 % - GI OTF
Irradiance Filter MAPE (%)
MPM FFNN
𝐺𝐼 > 100𝑊/𝑚2 2.01 % 1.56 %
𝐺𝐼 > 300𝑊/𝑚2 1.77 % 1.37 %
𝐺𝐼 > 600𝑊/𝑚2 1.06 % 1.24 %
Random 70:30 % - UCY OTF
Irradiance Filter MAPE (%)
MPM FFNN
𝐺𝐼 > 100𝑊/𝑚2 2.49 % 2.10 %
𝐺𝐼 > 300𝑊/𝑚2 2.36 % 1.84 %
𝐺𝐼 > 600𝑊/𝑚2 1.67 % 1.77 %
• Train subsets of 10, 30 and 70 % of the entire dataset
Results – Train subset duration
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MPM - Robust model for lowavailability duration datasets
ML - More accurate when using largertrain subsets
UCY OTF - Nicosia, CyprusGI OTF - Arizona, USA
10% 30% 70%10% 30% 70%
Results – Weather classification
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• Sixteen different classes (CL1 – CL16) based on the weather type and daytime• Type of weather based on the clearness index (𝑘𝑑) and its variability (𝑑𝑘𝑑)• Daily solar irradiance distribution in Arizona: Clear measurements 80.13 %,
Variable and Diffuse measurements 19.87 %
Daytime Type of weather
Clear Variable Diffuse Other
nighT CL13 CL9 CL5 CL1
Morning CL14 CL10 CL6 CL2
Noon CL15 CL11 CL7 CL3
Evening CL16 CL12 CL8 CL4
Clear Variable & Diffuse MAPE (%)
MPM FFNN
75 % 25 % 2.11 % 2.13 %
80 % 20 % 2.05 % 2.11 %
85 % 15 % 2.07 % 2.15 %
Random 10:30 % - GI OTF
ML & MPM – Lowest MAPE when using atrain subset containing approximately thesame amount of weather typemeasurements as the amount of theirradiance profile classes of the location
• Simple implementation (low complexity)
• 3 inputs parameter, 𝑃𝑅𝑑𝑐 output
• More accurate at high irradianceconditions
• Robust model at low availability durationdatasets
• Useful, meaningful coefficients (C1-C5)
• Higher complexity for implementation
• 4 inputs parameter, 𝑃𝑑𝑐 output
• More accurate at low and mediumirradiance conditions
• Higher training data partitions yieldmore accurate predictions
• No direct usable coefficients
Summary
12
Mechanistic Performance Model Machine Learning
Conclusions
13
• The MPM and the FFNN predictive models were compared in terms of:
o Input/output features (model complexity)
o Filtering criteria
o Train subset duration
o Irradiance profile classification
• For accurate predictions:
o Random train and test approach
o Irradiance condition filter
o Higher amount of train set
o Prevailing irradiance classes
• Future work will include an investigation regarding the resolution of data (i.e. 1-min,
15-min, and 60-min) that should be used to optimally train the models
Thank you for your attention
14
Andreas Livera
PV Technology LaboratoryUniversity of Cyprus Email: [email protected]
Acknowledgments
Want to learn more about the MPM?Wednesday, 17:00 – 18:30
Visual presentation 5CV_4_35
Presenter: Steve Ransome