An efficient energy prediction model for solar energy power system using Artificial Intelligence technique

Abstract

Prediction of Solar power generation plays an important role to improve the efficiency of economic dispatch function and reduce the dependence on fossil fuels and help in the energy management system. For time series solar energy prediction multiple models were introduced but these model trains are based on yearly historical data. A big data collection containing many missing values makes these model training more complicated that’s why In this paper, an efficient energy prediction model is proposed for the prediction of time series solar energy based on short predicted weather training data. Two complimentary models are based on linear regression and a knowledge based neural network is exploited to predict future solar power, with offline training. The LR is structured under the direction of the proposed input method parameter selection and used when training data is enough. KBNN is used for existing advantages predictive models are also very important when training data is not enough. According to test findings using real data sets. An LR model can deal effectively with linear data, but a KBNN model can cope effectively with nonlinear behavior. Additionally, the results demonstrate the effectiveness of LR showing a correlation coefficient (R2) is 98% with a root mean square error of 45 and KBNN shows a correlation coefficient (R2) is 99% with a root mean square error of 44 in providing a reliable version, The results additionally show the functionality of LR and KBNN in imparting a dependable version, especially when the short training dataset is available.