Analysis tools to improve model validation and

Analysis tools to improve
model validation and development
using monitoring data for
BIPV systems and PV systems operating in
challenging environments
Leon GAILLARDa,b , Guillaume RUEDINa, Stéphanie GIROUXa,
Syamimi SAADONa, Marc PLANTEVITc,
Christophe MENEZOa,b, Jean-François BOULICAUTc
a
CETHIL UMR CNRS 5008, INSA Lyon, Université Lyon 1
b Chaire INSA/EDF “Habitats & Energy Innovations”
c LIRIS CNRS UMR 5205, INSA Lyon
SOPHIA 2nd workshop on BIPV
15/09/2014
Outline
• RESSOURCES projet, recap of first results
• Data science tools: Knime software platform, clustering
• Analysis of electrical performance
• Analysis of thermal/aerodynamic
• Model validation with clustered data
• conclusions
SOPHIA 2nd BIPV workshop 15/09/2014
Leon GAILLARD (CETHIL)
1
Introduction
• Context: performance of PV and BIPV in challenging environments
• Challenge: effective approach to monitoring and simulation
– Need to unravel information in monitoring data
– Need to validate models with confidence
• Case in study: partially-transparent, ventilated PV double skin facades
building primary
wall
PV skin
(opaque PV)
PV skin
(opaque PV)
glazed wall
PV skin
(glazed/PV)
PV skin
PV skin
(glazed/PV)
(glazed/PV)
‘Summer’ configuration: natural ventilation to exterior
SOPHIA 2nd BIPV workshop 15/09/2014
Leon GAILLARD (CETHIL)
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RESSOURCES prototypes
HBS-Technal
double-skin
geometryPleated
Pleated
DSF facade
Width
ETNA-B
ETNA-A
Veranda/rooftop Facade/rooftop
Veranda/roof
Facade/roof
4m
3m
3m
Facade height
7.4 m
5.6 m
5.6 m
PV roof length
-
8.7 m (34° incline)
6.9 m (45° incline)
Air-gap depth
~0.6-0.8 m (prism)
0.7-0.44 m (roof) 3 m (veranda)
0.7 m
PV orientation
W.S.W.
S.W.
S.W.
SOPHIA 2nd BIPV workshop 15/09/2014
Leon GAILLARD (CETHIL)
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Instrumentation: HBS
Configuration: Vertically-pleated double skin facade
Site: occupied, glazed tertiary building in Toulouse, France (W.S.W. Wall)
PV arrays: stack of 3 arrays (blocs), cells integrated into S.W. oriented faces
Instrumentation: electrical output (constant loads); air and surface temperatures
(thermocouples); air flow (bi-directional anemometers); humidity; weather station (roof);
pyranometers on facade
SOPHIA 2nd BIPV workshop 15/09/2014
Leon GAILLARD (CETHIL)
4
RESSOURCES: first results
• Nominal behaviour follows daily and
seasonal cycles
• Robust stack effect observed in air gaps
• Anomalies in electrical power due to local
horizon effects
HBS bloc 3
Next steps: how can we make better use of the information contained in large datasets?
SOPHIA 2nd BIPV workshop 15/09/2014
Leon GAILLARD (CETHIL)
5
Data science analysis tools
K-means algorithm
1. Select initial partition with K clusters
2. Generate a new partition by assigning
each pattern to its closest cluster center
3. Compute new cluster centers
4. Repeat steps 2 and 3 until convergence
Hackl et al. Genome Biology 6 (2005) 13, R108
Cluster data according to distance metrics
Application to bioinformatics
Clustering of differentially expressed genes
during fat cell differentiation  global view on
biological processes and molecular networks
SOPHIA 2nd BIPV workshop 15/09/2014
Leon GAILLARD (CETHIL)
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Data analysis platform: Knime
SOPHIA 2nd BIPV workshop 15/09/2014
Leon GAILLARD (CETHIL)
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Analysis I: electrical performance
bloc 2 (middle)
• Electrical power for each array
• Non-trivial relationship between
measured in-plane radiation and power?
• Several correlation trends plus spurious
features
• Clustering data on environmental
conditions plus performance indicators:
Gh, Gcol, hPV,1, hPV,2, hPV,3
bloc 3 (lower)
Gaillard et al., Sol. En. 103 (2014) 223-241
bloc 1 (upper)
Gh irradiance
SOPHIA 2nd BIPV workshop 15/09/2014
Leon GAILLARD (CETHIL)
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Analysis I: electrical performance
bloc 1 (upper)
bloc 2 (middle)
bloc 3 (lower)
–
–
–
–
–
cluster
• algorithm differentiates structures found
in data (single analysis for all 3 blocs)
• data indirectly classified by
environmental conditions :
Blue & orange: nominal behaviour
Yellow: partially cloudy
Green : partial shadowing (evening)
Violet: nightime or overcast
Red: diffuse raidation only (orientation)
SOPHIA 2nd BIPV workshop 15/09/2014
Leon GAILLARD (CETHIL)
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Analysis II: thermal/aerodynamic response
Gaillard et al., J. Fund. Ren. En. App. 2 (2012) R120316
• Day/night airgap velocity profiles for sunny-calm and sunny-windy days
• Airflow patterns suggest physical phenomena
sunny day
sunny-windy day
• Extend analysis to larger dataset using
clustering algorithm
• Analyse data according to system
response rather than external conditions
SOPHIA 2nd BIPV workshop 15/09/2014
Leon GAILLARD (CETHIL)
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Analysis II: thermal/aerodynamic response
• Clustering using anemometer data in air cavity
• Dataset: sunny and partially cloudy days 21/06/2012 – 21/09/2012
Cluster
0
1
2
3
arithmetic mean (all data in cluster)
Vwind (m/s)
Ta (C)
Gh (W/m²) Gcol (W/m²) Gi,1 (W/m²) Gi,3 (W/m²)
2.7
3.2
2.1
1.3
25.7
27.2
21.6
17.9
391.1
407.4
209.8
104.8
307.3
444.8
178.8
138.1
165.8
462.5
37.9
17.6
136.1
408.5
26.9
10.9
Velocity profiles vary in dataset: different phenomena present?
SOPHIA 2nd BIPV workshop 15/09/2014
Leon GAILLARD (CETHIL)
11
Application to numerical model validation
• Simplified physical model developed in TRNSYS
• 1D energy balance + enthalpy balance  analytical solution for
temperature variation in airgap
• Single pressure loop model for mass flow rate (based on Hypri and
Brinkworth models)
g sin( )
 S Pth
  D'  0
m
3
D'  ( wd )3
wd
L 1
( f  ((1  K f 1 )  1))
L
D 2
SOPHIA 2nd BIPV workshop 15/09/2014
Leon GAILLARD (CETHIL)
C p
12
simu
•
•
•
•
data

m
Numerical model performance
Tf ,out
TPV,1
Model configured for RESSOURCES prototypes
Component behaviour predicted for measured environmental conditions
Model performance illustrated by correlation visualisation
Departures from observed behaviour caused by incoherence of model with
physical phenomena
SOPHIA 2nd BIPV workshop 15/09/2014
Leon GAILLARD (CETHIL)
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TPV,1
velocity profile
Tf ,out
Electrical response
simu
data

m
model validation with clustered data
• Cluster membership superposed on data-model comparison
• Model performance linked to observed system state
• Structures are associated to periods of different behaviour
SOPHIA 2nd BIPV workshop 15/09/2014
Leon GAILLARD (CETHIL)
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Discussion and conclusions
Data science tools applied to monitored (BI)PV installations
• System analysis and diagnostic
• Information for model validation
Ongoing work
• Evaluation of k-means algorithm
• Systematic approach to variable selection and cluster parameters
• Integration of advanced data-mining tools
• Detection of instrumentation faults and component malfunctions
SOPHIA 2nd BIPV workshop 15/09/2014
Leon GAILLARD (CETHIL)
15
Analysis tools to improve model validation and development
using monitoring data for BIPV systems and PV systems
operating in challenging environments
Thank you for your attention!
[email protected]

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