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

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• Introduction

• Motivation

• Methodology

• Results

• Conclusions

• Future Work

Introduction

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• 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

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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

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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

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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

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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

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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

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Mechanistic Performance Model Machine Learning

Conclusions

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• 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

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Andreas Livera

PV Technology LaboratoryUniversity of Cyprus Email: livera.andreas@ucy.ac.cy

Acknowledgments

Want to learn more about the MPM?Wednesday, 17:00 – 18:30

Visual presentation 5CV_4_35

Presenter: Steve Ransome