  The high variability of weather parameters is making photovoltaic energy
generation intermittent and narrowly controllable. Threatened by the sudden
discontinuity between the load and the grid, energy management for smart grid
systems highly require an accurate PV power forecasting model. In this regard,
Nonlinear autoregressive exogenous (NARX) is one of the few potential models
that handle time series analysis for long-horizon prediction. This later is
efficient and high-performing. However, this model often suffers from the
vanishing gradient problem which limits its performances. Thus, this paper
discus NARX algorithm for long-range dependencies. However, despite its
capabilities, it has been detected that this model has some issues coming
especially from the vanishing gradient. For the aim of covering these
weaknesses, this study suggests a hybrid technique combining long short-term
memory (LSTM) with NARX networks under the umbrella of Evolution of recurrent
systems 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 algorithm
outperform various benchmarked models.
