Application of cgpann in solar irradiance

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<ol><li> 1. Dr. Sahibzada Ali Mahmud Extracting Trends Ensembles in Solar Irradiance for Green Energy Generation using Neuro Evolution </li><li> 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 </li><li> 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 </li><li> 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. </li><li> 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 </li><li> 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 </li><li> 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. </li><li> 8. CGPANN (Cont.) Input to a particular node of a genotype g Output of a particular node p </li><li> 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 </li><li> 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 </li><li> 11. Sliding Window Mechanism </li><li> 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 </li><li> 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 </li><li> 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 </li><li> 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 </li><li> 16. Queries?? </li></ol>


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