Abstract
The usage of renewable energy sources has increased manifolds in terms of electric utilities. From other non-conventional 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.
Highlights
In recent years, the attention has been drawn towards the renewable energies
This paper aims to bring originality and predicts 10 days ahead solar power generation
The data set contains solar power (MW), solar irradiance (W/m2) and module temperature and these parameters are the inputs of the Recurrent Neural Network (RNN), Support Vector Machine (SVM) and Autoregressive with exogenous variable (ARX) model
Summary
The importance of wind and solar energies are at glance around the globe. Solar power systems and other renewable energy sources are widely used as a solution [2]. The solar energy is eco-friendly non-conventional sources which is why it is adopted widely. Industrial, commercial and residential applications can be operated by the solar energy sufficiently [3]. The nature of solar energy is sporadic and unpredictable due to which errors can be caused to the power grid. It can greatly limits the large-scale integration of PV power generation system to the power or smart grid.
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