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.