%0 Journal Article %1 noauthororeditor %A Bhargava, Harendra Kumar Verma | Ashish %D 2021 %J International Journal of Trend in Scientific Research and Development %K Renewable artificial energy intelligence network neural survey %N 4 %P 1560-1567 %T Solar Irradiation Prediction using back Propagation and Artificial Neural Network %U https://www.ijtsrd.comengineering/electrical-engineering/43673/solar-irradiation-prediction-using-back-propagation-and-artificial-neural-network/harendra-kumar-verma %V 5 %X Solar Energy is one of most promising potential renewable sources of energy. But among all the conventional sources of renewable energy, its nature is quite unpredictable owing to the fact that the solar irradiation keeps on changing and fluctuating. This leads to uncertainty in ascertaining the exact measure of solar power that can be harnessed. Hence forth, a solar irradiation model based on solar irradiation using ANN shall aid in preliminary estimation of the measure of solar power energy that is available for usage incorporating the load dispatch and grid. This proposed work of study puts forward an Artificial Intelligence based model of a Solar Irradiation Forecasting implemented by Artificial Neural Networks ANN . ANN is utilized accredited to its adaptive, data driven and nonlinear nature that exhibits high efficacy in forecasting domains. An endeavor has been also made for solar irradiation forecast using the ANN along with Levenberg Marquardt LM algorithm. The primary reason for using the Levenberg Marquardt algorithm is the fact that is an extremely effective algorithm employing back propagation. The attributes of the algorithm are high stability and speed which yields lesser number of iterations and relatively less magnitude of errors. The evaluation of the proposed system needs to be done on performance specific parameters which in this case are chosen as Mean Absolute Percentage Error MAPE and Regression. Mean absolute percentage error indicates the amount of errors present in the prediction which is to say the deviation between the actual targets and predicted output. Another parameter which validates the mean absolute percentage error is the regression which is a graphical representation of discrete values in the target set and the predicted output. Additional analysis mechanisms such as training states has been also presented which depicts how the mean square error plummets as the number of iterations increase. The variation of mean square error can be seen in training, testing and validation phases. The neural network topology used is 1 20 1 indicating one neuron in the input layer, 20 neurons in the hidden layer and 1 neuron in the output layer respectively. It has been shown that the proposed methodology attains a very good accuracy of approximately 97.74 with the error rate amounting to a meager 2.76 . This model serves to be a robust mechanism and shows good performance. The low error and high accuracy can be attributed to the efficacy of back propagation in Artificial Neural Networks. A comparative analysis is also presented with contemporary work that attains an error of 30 , proving the fact that the proposed system outperforms the contemporary techniques. Harendra Kumar Verma | Ashish Bhargava "Solar Irradiation Prediction using back Propagation and Artificial Neural Network" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd43673.pdf Paper URL: https://www.ijtsrd.comengineering/electrical-engineering/43673/solar-irradiation-prediction-using-back-propagation-and-artificial-neural-network/harendra-kumar-verma

@article{noauthororeditor, abstract = {Solar Energy is one of most promising potential renewable sources of energy. But among all the conventional sources of renewable energy, its nature is quite unpredictable owing to the fact that the solar irradiation keeps on changing and fluctuating. This leads to uncertainty in ascertaining the exact measure of solar power that can be harnessed. Hence forth, a solar irradiation model based on solar irradiation using ANN shall aid in preliminary estimation of the measure of solar power energy that is available for usage incorporating the load dispatch and grid. This proposed work of study puts forward an Artificial Intelligence based model of a Solar Irradiation Forecasting implemented by Artificial Neural Networks ANN . ANN is utilized accredited to its adaptive, data driven and nonlinear nature that exhibits high efficacy in forecasting domains. An endeavor has been also made for solar irradiation forecast using the ANN along with Levenberg Marquardt LM algorithm. The primary reason for using the Levenberg Marquardt algorithm is the fact that is an extremely effective algorithm employing back propagation. The attributes of the algorithm are high stability and speed which yields lesser number of iterations and relatively less magnitude of errors. The evaluation of the proposed system needs to be done on performance specific parameters which in this case are chosen as Mean Absolute Percentage Error MAPE and Regression. Mean absolute percentage error indicates the amount of errors present in the prediction which is to say the deviation between the actual targets and predicted output. Another parameter which validates the mean absolute percentage error is the regression which is a graphical representation of discrete values in the target set and the predicted output. Additional analysis mechanisms such as training states has been also presented which depicts how the mean square error plummets as the number of iterations increase. The variation of mean square error can be seen in training, testing and validation phases. The neural network topology used is 1 20 1 indicating one neuron in the input layer, 20 neurons in the hidden layer and 1 neuron in the output layer respectively. It has been shown that the proposed methodology attains a very good accuracy of approximately 97.74 with the error rate amounting to a meager 2.76 . This model serves to be a robust mechanism and shows good performance. The low error and high accuracy can be attributed to the efficacy of back propagation in Artificial Neural Networks. A comparative analysis is also presented with contemporary work that attains an error of 30 , proving the fact that the proposed system outperforms the contemporary techniques. Harendra Kumar Verma | Ashish Bhargava "Solar Irradiation Prediction using back Propagation and Artificial Neural Network" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd43673.pdf Paper URL: https://www.ijtsrd.comengineering/electrical-engineering/43673/solar-irradiation-prediction-using-back-propagation-and-artificial-neural-network/harendra-kumar-verma }, added-at = {2021-07-15T13:12:40.000+0200}, author = {Bhargava, Harendra Kumar Verma | Ashish}, biburl = {https://www.bibsonomy.org/bibtex/2f60fbb8b0f1dcbf9683c8a885b50f3ba/ijtsrd}, interhash = {165118aa3e44a620525b89364cc1f320}, intrahash = {f60fbb8b0f1dcbf9683c8a885b50f3ba}, issn = {2456-6470}, journal = {International Journal of Trend in Scientific Research and Development}, keywords = {Renewable artificial energy intelligence network neural survey}, language = {english}, month = jun, number = 4, pages = {1560-1567}, timestamp = {2021-07-15T13:12:40.000+0200}, title = {Solar Irradiation Prediction using back Propagation and Artificial Neural Network }, url = {https://www.ijtsrd.comengineering/electrical-engineering/43673/solar-irradiation-prediction-using-back-propagation-and-artificial-neural-network/harendra-kumar-verma}, volume = 5, year = 2021 }