A Neuro-evolutionary approach for extracting trend ensembles in the solar irradiance patterns for renewable electric power generation. Cartesian Genetic Programming Evolved Artificial Neural Network (CGPANN) is developed and trained for hourly and 24-hourly prediction. The System takes Solar Irradiance data as its only Input parameter And it is 95.48% accurate in solar irradiance prediction

1. Dr. Sahibzada Ali Mahmud
Extracting
Trends Ensembles in
Solar Irradiance
for
Green Energy Generation
using
Neuro Evolution

2. Abstract
A Neuro-evolutionary approach for extracting trend ensembles in
the solar irradiance patterns for renewable electric power
generation.
Cartesian Genetic Programming Evolved Artificial Neural Network
(CGPANN) is developed and trained for hourly and 24-hourly
prediction.
The System takes Solar Irradiance data as its only Input parameter
And it is 95.48%accurate in solar irradiance prediction

3. Introduction
Owing to the rising fossil fuel costs and
the Degradation of Atmosphere by
these fossil fuels, there is a dire need
for economical and effective harnessing
and utilization of Solar Energy.
Moreover,,
Most places on the earths surface
receive clean and abundant solar
energy, free of cost, throughout the
year.
Fuel Used for Generating
Electric Power
Coal Natural gas Nuclear
Other Renewable Biomass Geothermal
Solar Wind Petroleum

4. Portion we are adding
Our work mainly focuses on introducing a new and
efficient solution based on the Neuro-evolutionary
technique termed as the Cartesian Genetic
Programming Evolved Artificial Neural Network
(CGPANN) to extract the trend ensembles in the solar
irradiance patterns for Renewable Power Generation.

5. Solar Irradiance and Current
Models
Solar irradiance is a measure of
the irradiance (power per unit area on the
Earth's surface) produced by the Sun in the
form of electromagnetic radiation, which is
perceived by humans as sunlight.
It is Measured in W/m2.
Techniques used for forecasting Trends of
the solar irradiance include;
• Stochastic techniques
• Probabilistic methods
• Machine learning applications

6. Cartesian Genetic Programming Evolved
ANN
CGP is a state of the are genetic program representation that utilizes a two dimensional
programming architecture that is incorporated by nodes or genes

7. CGPANN (Cont.)
In CGP, a genotype is represented by a string of integers with the
corresponding phenotype a two dimensional nodal network.
The genotype is evolved by changing the connectivity and functions of
nodes in the network, thus obtaining a range of topologies.

8. CGPANN (Cont.)
Input to a particular node of a genotype ‘g’
Output of a particular node ‘p’

9. The solar irradiance data of 1997, collected
from Saudi Arabian stations in Al Ahsa.
Al-Ahsa lies at an altitude of 178 m,
25.3o N latitude and 49.48o E longitude.
The average temperature is 304K, relative
humidity 23% and the daily average solar
global radiation is 21.6MJ/m2
Solar Irradiance Patterns – Case Study

10. Simulation Attributes
• Number of inputs taken (24 instances hourly spaced)
• Training Data Set (1 year)
• Generation evolved (1 Million)
• Accuracy and Error Calculation
• Fitness

11. Sliding Window Mechanism

12. A phenotype was being translated by the
genotype that showed the best fitness
level by training on 24hrs data as system
input for 1 year.
All the network nodes however may not
be utilized by the final phenotype. Usually
5 to 10 percent of nodes participate in
the production of the phenotype.
Training Session

13. Testing Results for various Number of Nodes for 24 Hours Input Network
and Prediction of the Next 12 hours, 1 day, 2 Days, and 1 Week, for 24-
hourly Prediction Training, Giving 98.5% Accuracy..
Testing Session
Testing sessions’ Percentage Accuracies

14. Actual Verses Forecasted Terrain
• Graph showing Actual and forecasted terrain of solar irradiance for 300hrs, Network initialized
with 500 Nodes with a 93% genotype redundancy

15. • The paper uses CGPANN prediction model for forecasting the Solar
irradiance pattern
• The Model is fair enough in extracting trends ensembles from the
given time devised terrain by achieving as high as 95.5% Mean
Absolute Accuracy
• The work is a step towards the new age of exploring renewable
energy resources for Mankind
Conclusion

16. Queries??