This presentation offers insight into use of ANN and machine learning for various applications in solar energy. Prepared and presented by Dr. Ali H. A. Alwaeli.

1. Solar Energy Research Institute (SERI)
National University of Malaysia (UKM)

2. Contents
PV/T
LEARNING
MODEL
PERFORMANCE
PREDICTION

3. PV/ T
C

4. PV/ T
C

5. PV/ T
Cell temperature
▲
Voltage ▼
Power ▼
Efficiency ▼
C

6. PV/ T
C

7. PV/ T (1) Cooling of PV module
(2) Produce thermal energy
C

8. PV/ T
Flat Plate
Solar Collector
Photovoltaic
Panel
Photovoltaic thermal
Solar Collector
C

9. PV/ T
 Area of Collector = Area of thermal collector (At)
+Area of photovoltaic panel (Apv)
 Efficiency = ( thermal efficiency (t) + Electrical
efficiency (el))2
Thermal Efficiency = 60 %
Electrical Efficiency = 10 %
Combined Photovoltaic Thermal Efficiency = 35 %
 Area of Collector = Area of thermal collector
(At) +Area of photovoltaic panel (Apv)
 Efficiency = thermal efficiency (t) + Electrical
efficiency (el)
Thermal Efficiency = 50 %
Electrical Efficiency = 14 %
Combined Photovoltaic Thermal Efficiency = 64 %
ST PV PV/T
Flat Plate Solar
Collector
C

10. PV/ T

11. PV/ T
C

12. PV/ T
???
C

13. PV/ T
Mathematical
Modelling
Numerical
simulation
Conclusion
Real-life problem
Real-life solution Interpretation
description
Experimenta
l Validation
C

14. PV/ T
• The electric power (P) is expressed as:
Where (Imp) is the current and (Vmp) is the voltage. The power unit is watts (W).
• The electrical efficiency (e) of conventional PV is calculated using the formula below:
Where (G) is the solar irradiance in w/m2 and (Amodule) is the PV module area in m2
𝑷 𝒎𝒂𝒙 = 𝑰 𝒎𝒑 × 𝑽 𝒎𝒑
 𝒆
=
𝑷 𝒎𝒂𝒙
𝑮 × 𝑨 𝒎𝒐𝒅𝒖𝒍𝒆

15. PV/ T
• The total of the efficiencies, which is known as total efficiency combined is used to
evaluate the overall performance of the system:
• The thermal efficiency (th) of the conventional flat plate solar collector is calculated
using the formula below:
• For temperature-dependent electrical efficiency of the PV module, el the expression
is given as below:
elthcombined  
c(t)
u
ht
A*I
Q
=  )( aiLRcu TTUSFAQ 
 )TT(*= rpmrel   1
Hottel–Whillier
equations

16. PV/ T
C

17. PV/ T
C

18. PV/ T
INDOOR TESTING

19. PV/ T
OUTDOOR
TESTING
Alzaabi, A. A., Badawiyeh, N. K., Hantoush, H. O., & Hamid, A. K. (2014).
Electrical/thermal performance of hybrid PV/T system in Sharjah, UAE. International
Journal of Smart Grid and Clean Energy, 3(4), 385-389. C

20. PV/ T
Rahman, S., Sarker, M. R. I., Mandal, S., & Beg, M. R. A. (2018). Experimental and Numerical Analysis of a
Stand-Alone PV/T System to Improve its Efficiency. Sch J Appl Sci Res, 1, 28-33. C

21. PV/ T
Electrical
Thermal
Cost
Solar
Irradiance
Ambient
temperature
Wind speed
Mass flow
rate
Inlet fluid
temperature
Humidity
Dust
Accumulation
Working fluid
Pipe width
and thickness
Outlet fluid
temperature
etc.

22. PV/ T
Electrical
Electrical
efficiency &
power
IV curve
characteristics
Electrical Exergy
& Exergy
efficiency
Thermal
Thermal
efficiency &
power
Optical
efficiency &
mass flow rate
Thermal Exergy
& Exergy
efficiency
Cost Life Cycle Costs Cost of Energy Payback Period

23. Contents
PV/T
LEARNING
MODEL
PERFORMANCE
PREDICTION

24. Artificial Neural Networks (ANN)
C

25. Artificial Neural Networks (ANN)
Motivation

26. Artificial Neural Networks (ANN)
Advantages

27. Artificial Neural Networks (ANN)
Components

28. Artificial Neural Networks (ANN)
C

29. Artificial Neural Networks (ANN)
C

30. Artificial Neural Networks (ANN)
C

31. Artificial Neural Networks (ANN)
Numerical
Data
Labeling
Images
Video
Text
Time-series
Clustering
Machine
perception
Classification Clustering
C

32. Artificial Neural Networks (ANN)
Classification
SUPERVISED LEARNING
Face detection
Object
Identification
Text
classification
Voice detection
Gesture
Identification
Transcript
speech-text
Create
Numerical
Data
Assign
to
data
TRAIN
THE
ANN
Create
correlation

33. Artificial Neural Networks (ANN)
Ni, D. X. (2007). Application of neural networks to character recognition. Proceedings
of students/faculty research day, CSIS, Pace University, May 4th.
Classification
C

34. Artificial Neural Networks (ANN)
Classification
https://www.analyticsindiamag.com/how-to-create-your-first-artificial-neural-network-in-python/ C

35. Artificial Neural Networks (ANN)
Regression
SUPERVISED LEARNING
Hardware
breakdown
Employee
turnover
Customer churn
Health
breakdown
Machine output
C

36. Artificial Neural Networks (ANN)
Regression
https://dataaspirant.com/2017/03/02/how-logistic-regression-model-works/ C

37. Artificial Neural Networks (ANN)
Regression
https://dataaspirant.com/2017/03/02/how-logistic-regression-model-works/ C

38. Artificial Neural Networks (ANN)
Regression
https://dataaspirant.com/2017/03/02/how-logistic-regression-model-works/ C

39. Artificial Neural Networks (ANN)
Clustering
UNSUPERVISED
LEARNING
Comparing documents, images, etc.
to surface similar items
Detecting anomalies, or unusual
behavior
C

40. Artificial Neural Networks (ANN)
Clustering
C

41. Artificial Neural Networks (ANN)
REINFORCEMENT
Self-driving car
Customer management
Adaptive traffic signal
control
Games
Reinforcement
LEARNING
C

42. Artificial Neural Networks (ANN)
Learning paradigms
SUPERVISED LEARNING
UNSUPERVISED LEARNING
REINFORCEMENT
LEARNING

43. Artificial Neural Networks (ANN)
Learning paradigms
Kaplan, S. (2017). Deep generative models for synthetic retinal image generation. C

44. Artificial Neural Networks (ANN)
Learning paradigms
Unsupervised learning Supervised learning Clustering: An Introduction to Supervised
Machine Learning and Pattern Classification: The Big Picture by Sebastian Raschka C

45. Artificial Neural Networks (ANN)
SINGLE LAYER
C

46. Artificial Neural Networks (ANN)
MULTIPLE LAYER PERCEPTRON (MLP)
C

47. Artificial Neural Networks (ANN)
MULTIPLE LAYER PERCEPTRON (MLP)
C

48. Artificial Neural Networks (ANN)
MULTIPLE LAYER PERCEPTRON (MLP)
C

49. Artificial Neural Networks (ANN)
MULTIPLE LAYER PERCEPTRON (MLP)
Jain & Mao, 1996 C
Deep

50. Artificial Neural Networks (ANN)
MULTIPLE LAYER PERCEPTRON (MLP)
A MLP consists of at least three layers of nodes: an input layer, a hidden layer and an
output layer. Except for the input nodes, each node is a neuron that uses a
nonlinear activation function. MLP utilizes a supervised learning technique
called backpropagation for training. Its multiple layers and non-linear activation distinguish
MLP from a linear perceptron. It can distinguish data that is not linearly separable.

51. Artificial Neural Networks (ANN)
MULTIPLE LAYER PERCEPTRON (MLP)
changing connection weights after each piece of data is processed, based on the
amount of error in the output compared to the expected result.

52. Artificial Neural Networks (ANN)
MULTIPLE LAYER PERCEPTRON (MLP)
MLPs are useful in research for their ability to solve problems stochastically, which often
allows approximate solutions for extremely complex problems like fitness approximation.
MLPs are universal function approximators as shown by Cybenko's theorem, so they can
be used to create mathematical models by regression analysis. As classification is a
particular case of regression when the response variable is categorical, MLPs make good
classifier algorithms.
MLPs were a popular machine learning solution in the 1980s, finding applications in
diverse fields such as speech recognition, image recognition, and machine
translation software, but thereafter faced strong competition from much simpler (and
related) support vector machines. Interest in backpropagation networks returned due to
the successes of deep learning.

53. Artificial Neural Networks (ANN)
MULTIPLE LAYER PERCEPTRON (MLP)

54. Artificial Neural Networks (ANN)
FEED-FORWARD
Connections between the nodes do not form a cycle.
The first and simplest type of artificial neural network
devised.

55. Artificial Neural Networks (ANN)
BACKPROPAGATION

56. Artificial Neural Networks (ANN)
BACKPROPAGATION

57. Artificial Neural Networks (ANN)
BACKPROPAGATION
 Supervised learning
 Used at each layer to minimize the
error between the layer’s response
and the actual data
 The error at each hidden layer is an
average of the evaluated error

58. Artificial Neural Networks (ANN)
BACKPROPAGATION
 N is a neuron.
 Nw is one of N’s inputs weights
 Nout is N’s output.
 Nw = Nw +Δ Nw
 Δ Nw = Nout * (1‐ Nout)* NErrorFactor
 NErrorFactor = NExpectedOutput – NActualOutput
 This works only for the last layer, as we can know the actual output, and
the expected output.

59. Artificial Neural Networks (ANN)
Training stage
Validating stage
Testing stage

60. Contents
PV/T ANN
PERFORMANCE
PREDICTION

61. PERFORMANCE
PREDICTION
Methodology
Performance
Prediction
Statistical
methods
Analysis

62. PV/T modeling and performance
prediction
• Why use artificial neural networks (ANN) in solar energy
technologies?
• How to use artificial neural networks (ANN) in solar energy
technologies?
• How to utilize regression and classification to invest in solar energy?

63. PV performance time-series prediction
Short-term power
forecasting
Estimating power loss
due to environment
Estimating the energetic
performance of PV/T
PV fault detection
Optimize PV array
inclination
Solar Energy Forecasting
Optimize maximum
power point tracking

64. PV performance time-series prediction
C
Senkal, O. & Kuleli, T. (2009). Estimation of solar radiation over Turkey using
artificial neural network and satellite data. Applied Energy, Vol. 86, pp. 1222–

65. PV performance time-series prediction
Almonacid, F., Rus, C., Hontoria, L., Fuentes, M. & Nofuentes G. (2009). Characterisation
of
Si-crystalline PV modules by artificial neural Networks. Renewable Energy, Vol. 34, C

66. PV performance time-series prediction
EXAMPLE 1: PREDICTIVE
PERFORMANCE OF SOLAR ENERGY
SYSTEM
Yaïci, W., Longo, M., Entchev, E., & Foiadelli, F. (2017).
Simulation study on the effect of reduced inputs of artificial
neural networks on the predictive performance of the solar
energy system. Sustainability, 9(8), 1382.
Multi-Layer Perceptron (MLP) Feed Forward (FF) and back propagation
(BP) neural network structure
Comparison with experimental data from a solar energy system tested in
Ottawa, Canada during two years under different weather conditions

67. PV performance time-series prediction
C
Example 1

68. PV performance time-series prediction
Example 1
C

69. PV performance time-series prediction
Example 1
C

70. PV performance time-series prediction
Firefly algorithm (FA) and Particle Swarm Optimization (PSO) applied
to train NN
EXAMPLE 2: SHORT TERM POWER
FORECASTING
Demirdelen, T., Aksu, I. O., Esenboga, B., Aygul, K.,
Ekinci, F., & Bilgili, M. (2019). A new method for
generating short-term power forecasting based on artificial
neural networks and optimization methods for solar
photovoltaic power plants. In Solar Photovoltaic Power
Plants (pp. 165-189). Springer, Singapore.
Multi-Layer Feed Forward (MLFF) neural network structure
The data of 1MWPV power plant in Turkey is used to estimate output power
by
real-time data mining for short time prediction.

71. PV performance time-series prediction
Example 2
C

72. PV performance time-series prediction
Example 2
Ambient temperature [◦C]
Solar radiation [W/m2]
PV Panel temperature [◦C]
Input
Layer
Hidden
Layer
Output
Layer
24 8
Interconnections Interconnections
PV power (W)

73. PV performance time-series prediction
Example 2
During network training, 41 parameters are trained. The algorithms are run for
300 iterations, and during the training, 20 individuals are used for each optimization
method. A total of 100479 data sets are sent to the network.
Weights for the 24 interconnections between the input and the hidden layer
The bias value for the 8 neurons in hidden layers
Weights for the 8 interconnections between the hidden and the output layer
The bias value for the single output layer
The training coefficients during network training
Procedure

74. PV performance time-series prediction
Example 2
C

75. PV/T performance time-series prediction
Particle Swarm Optimization (PSO) applied to train NN
EXAMPLE 3: ESTIMATING ENERGETIC
PERFORMANCE
Alnaqi, A. A., Moayedi, H., Shahsavar, A., & Nguyen, T.
K. (2019). Prediction of energetic performance of a
building integrated photovoltaic/thermal system thorough
artificial neural network and hybrid particle swarm
optimization models. Energy conversion and
management, 183, 137-148.
Multi-Layer Feed-Forward Back-Propagation (FFBP) neural network
structure
building integrated photovoltaic/thermal system

76. PV/T performance time-series prediction
the total rate of useful thermal and electrical energy
available from the BIPV/T system to the heating load of
the fresh air
Evaluation criteria
Performance Evaluation
Criteria (PEC)
Input Output
Channel
length
Channel
depth
Channel
width
Air mass flow
rate
PEC
Example 3

77. PV/T performance time-series prediction
Example 3
C

78. PV/T performance time-series prediction
Example 3
C

79. PV/T performance time-series prediction
Example 3
C

80. Photovoltaic Thermal (PV/T) Collector

81. PV/T modeling and performance prediction

82. PV/T modeling and performance prediction

83. PV/T modeling and performance prediction
Al-Waeli et al. (2019). Artificial neural network modeling and analysis of
photovoltaic/thermal system based on the experimental study.

84. PV/T modeling and performance prediction
Al-Waeli et al. (2019). Artificial neural network modeling and analysis of
photovoltaic/thermal system based on the experimental study.

85. Statistical methods
• Coefficient of determination
• Mean Absolute Percentage Error
• Root Mean Square Error
• Mean Absolute Error
• Mean Square Error
difference between two continuous variables
how well a regression model is capable of describing a
data set
a measure of prediction accuracy of a forecasting
method in statistics,
a measure of the quality of an estimator
measure of the differences between
an estimator and the values observed

86. Statistical methods
COEFFICIENT OF DETERMINATION
a measure for how well a regression model is capable of
describing a data set.
used to evaluate the validity of the predictive
results compared to the actual (experimental)
real model results
𝒚𝒊= experimental value of (y)
𝒇𝒊= predicted value of (y)
𝒚𝒊= the mean of the experimental values
N = the number of values

87. Statistical methods
MEAN ABSOLUTE ERROR
a measure of difference between two continuous variables.
the average vertical distance between each point and the
identity line. MAE is also the average horizontal distance
between each point and the identity line.

88. Statistical methods
MEAN ABSOLUTE PERCENTAGE ERROR
a measure of prediction accuracy of a forecasting
method in statistics,

89. Statistical methods
MEAN SQUARE ERROR
measures the average of the squares of the errors—that is, the average
squared difference between the estimated values and what is estimated
The MSE is a measure of the quality of an estimator—it is always non-
negative, and values closer to zero are better.

90. Statistical methods
ROOT MEAN SQUARE ERROR
a measure for the difference between a data set and a corresponding fit. The RMS
asymptotically converges to the standard deviation from the model's predicted value
for sufficiently large sizes of data sets.
measure of the differences between values (sample or population values)
predicted by a model or an estimator and the values observed.

91. Statistical methods
Al-Waeli et al. [2018]. Comparison of prediction methods of PV/T nanofluid and nano-PCM system
using a measured dataset and artificial neural network

92. Statistical methods
Al-Waeli et al. [2018]. Comparison of prediction methods of PV/T nanofluid and nano-PCM system
using a measured dataset and artificial neural network

93. Statistical methods
Al-Waeli et al. [2018]. Comparison of prediction methods of PV/T nanofluid and nano-PCM system
using a measured dataset and artificial neural network

94. Statistical methods
Al-Waeli et al. [2018]. Comparison of prediction methods of PV/T nanofluid and nano-PCM system
using a measured dataset and artificial neural network
LOWER IS BETTER

95. Food for Thought
• What is the difference between Artificial Intelligence and
computational intelligence?
• What is Particle Swarm Optimization?
• What is Firefly Algorithm?
• Which is a better algorithm for optimization? Why?
• How to interpret skewness and kurtosis?
• How to select the appropriate number of hidden layers?

96.  Initial weights (small random values ∈[‐1,1])
 Transfer function (How the inputs and the weights are combined to
produce output?)
 Error estimation
 Weights adjusting
 Number of neurons
 Data representation
 Size of training set
The How?

97. Text book in PV/T
https://www.amazon.com/Photovoltaic-Thermal-Systems-Principles-Applications/dp/3030278239/ref=sr_1_1?keywords=PV%2FT+al-
waeli&qid=1563014343&s=gateway&sr=8-1

98. Review article in PV/T

99. Original article on ANN use for PV/T
Performance Prediction

100. Thank you

101. Follow me on social media
www.linkedin.com/in/ali-al-waeli-
a76a33124
https://www.researchgate.net/profile/Ali_Al-Waeli
https://www.facebook.com/AliHWaeliAR/
https://www.facebook.com/AliHwaeli/

102. Disclaimer
Some of the figures/tables in this presentation are not owned by the
Presenter, they are material copyrighted to their rightful owners. This
presentation is intended for non-profit educational purposes.
Slides with copyrighted material (images/tables) contain the letter C in the
bottom right corner
The actual presentation contains elements that are not mentioned in the
PowerPoint and even edits to the PowerPoint. Still, this presentation contain
useful information and figures with regards to machine learning, artificial
neural networks and photovoltaic thermal (PV/T) collectors
This presentation was prepared independently by the presenter and is
owned by: Dr. Ali H. A. Alwaeli