The usage of renewable energy sources has increased manifolds in terms of electric utilities. From other nonconventional sources, solar energy has been seen as a promising and convenient source used around the globe. In terms of meeting the daily requirements, solar energy has huge potential to fulfil the world's demand. However, firstly the characteristic of solar energy is very unpredictable and intermittent due to variation of weather. Secondly, the optimization and the planning of smart grid effect the operation of PV system. Thus, prediction on the long horizon is needed to address this problem. Nevertheless, long term forecasting of solar power generation is deliberated as a challenging problem. Therefore, this paper proposes a 10 day ahead solar power forecasting using combination of linear and non-linear machine learning models. At first, the outputs are generated from Recurrent Neural Network (RNN), Support Vector Machine (SVM) and Autoregressive with exogenous variable (ARX). Later on, these three outputs are combined and are made as a strong classifier with the Adaptive boost (Adaboost) algorithm. The simulations were conducted on the data obtained from real PV plant. By the experimental results and discussion, it was endogenously concluded that the combination of all techniques with the Adaboost have increased the performances and showing the high accuracy as compare to the individual machine learning models. The hybrid Adaboost shows %MAPE 8.88, which proven high accuracy. While on the other hand, for the individual technique, RNN shows 10.88, SVM reveals 11.78 and ARX gives 13.00 of percentage MAPE. The improvement proves that combination of techniques performs better than individual models and proclaims the high accuracy.

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Energy in any form is a vital source of producing electricity for daily utilization. Wind energy source as renewable energy is playing a pivotal role in generating power from electric gird owing to environmentally friendly feature. Due to the volatile and intermittent nature of wind energy, fluctuations and disparities occur in installing, monitoring, and planning in an energy management system. Therefore, forecasting and prediction are promising solutions to address mismanagement at the grid. Consequently, machine learning tools specifically neural networks have created a huge impact in forecasting wind power. In this study, the feed-forward neural network is adopted for predicting wind power. Additionally, for having precise and efficient results, different training models i.e. one-step sacent, resilient propagation, Bayesian regularization, scaled-conjugate gradient back propagations, and Levenberg-Marquardt are used to make the comparative analysis. From the simulations and results, it was concluded that Bayesian regularization training model is performing best and achieving high accuracy by obtaining 1.66 of RMSE and 6.06 of %MAPE. Eventually, it is concluded that neural networks can be a good choice to predict wind power for optimal solutions. Moreover, the proposed model can be applied to other renewable energy source predictions.

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