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The high variability of weather parameters is making photovoltaic energygeneration intermittent and narrowly controllable. Threatened by the suddendiscontinuity between the load and the grid, energy management for smart gridsystems highly require an accurate PV power forecasting model. In this regard,Nonlinear autoregressive exogenous (NARX) is one of the few potential modelsthat handle time series analysis for long-horizon prediction. This later isefficient and high-performing. However, this model often suffers from thevanishing gradient problem which limits its performances. Thus, this paperdiscus NARX algorithm for long-range dependencies. However, despite itscapabilities, it has been detected that this model has some issues comingespecially from the vanishing gradient. For the aim of covering theseweaknesses, this study suggests a hybrid technique combining long short-termmemory (LSTM) with NARX networks under the umbrella of Evolution of recurrentsystems with optimal linear output (EVOLINO). For the sake of illustration,this new approach is applied to PV power forecasting for one year in Australia.The proposed model enhances accuracy. This made the proposed algorithmoutperform various benchmarked models.

A Novel Approach Based Deep RNN Using Hybrid NARX-LSTM Model For Solar Power Forecasting