artificial neural networks (ANN) are used for many years to optimize the results of various problems in various sectors and disciplines like, Engineering, Industrial applications, Finance, Medical applications, Economy, Forecasts, etc. The training ability of ANN has capability to deal with nonlinear and complicated issues termed for its utilization to solve projection troubles. In this paper we have developed a nonlinear auto regressive or NARX model with exogenous input, for the forecasting of AC system output of a solar power plant placed in RCEW college jaipur (Rajasthan) for the year 2014. In this system for the purpose of forecasting, the Levenberg-Marquardt (LM) optimization approach was utilized as it aids the finest training rate pursued as a back propagation algorithm for the multilayer feed forward ANN model using MATLAB® R2013 ANN time series toolbox. The outcome of the model concluded that the preferred procedure is powerful in prediction of forthcoming future energy generation demands for the daily operational planning of solar power generation.
Sharma, G., Pandey, A., & Chaudhary, P. (2016). Prediction of Output Solar Power Generation using Neural Network Time Series Method. The International Conference on Electrical Engineering, 10(10th International Conference on Electrical Engineering ICEENG 2016), 1-5. doi: 10.21608/iceeng.2016.30308
MLA
Garima Sharma; Alok Pandey; Pravesh Chaudhary. "Prediction of Output Solar Power Generation using Neural Network Time Series Method", The International Conference on Electrical Engineering, 10, 10th International Conference on Electrical Engineering ICEENG 2016, 2016, 1-5. doi: 10.21608/iceeng.2016.30308
HARVARD
Sharma, G., Pandey, A., Chaudhary, P. (2016). 'Prediction of Output Solar Power Generation using Neural Network Time Series Method', The International Conference on Electrical Engineering, 10(10th International Conference on Electrical Engineering ICEENG 2016), pp. 1-5. doi: 10.21608/iceeng.2016.30308
VANCOUVER
Sharma, G., Pandey, A., Chaudhary, P. Prediction of Output Solar Power Generation using Neural Network Time Series Method. The International Conference on Electrical Engineering, 2016; 10(10th International Conference on Electrical Engineering ICEENG 2016): 1-5. doi: 10.21608/iceeng.2016.30308
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