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The increasing penetration of photovoltaic (PV) systems into the electrical energy systems brings forward several technical and economic issues that mostly relate to their unpredictable nature. A promising solution to many of these is the implementation of robust PV generation forecasting models. In this paper a novel hybrid Ensemble Long Short-Term Memory-Feed Forward Neural Network (ELSTM-FFNN) model is proposed, that is able to perform both very-short and short-term forecasting. The performance of the proposed model is compared with individual LSTM models, and its forecasting accuracy is assessed in two different forecasting horizons: (a) 15-min ahead and (b) 1-h ahead. Moreover, in order to fully examine the contribution of the utilized data to the performance of the model, several scenarios have been formulated for each forecasting horizon. The results indicate that the proposed ELSTM-FFNN model can increase the forecasting accuracy in both horizons between 3-11.9% and 0.2-17.8%, respectively, considering the Mean Absolute Range Normalized Error (MARNE).This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Summary This article deals with the problem of energy losses in distribution networks (DNs) under electric vehicle (EV) penetration. The problem of charging overlaps causing severe power losses and voltage drops is faced under appropriate EV charging. An optimized EV charging schedule is proposed under EV time‐of‐arrival consideration. The solution algorithm is based on a particle swarm optimization (PSO) variant, and both grid‐to‐vehicle (G2V) and vehicle‐to‐grid (V2G) schemes are included regarding the power interactions of the EVs with the grid. The goal is to optimally allocate the charging and/or discharging time periods of the EVs in order to minimize energy losses while delivering fully charges EVs to the owners. New departures after the first EVs' arrival are also handled. The results indicate that with the proposed scheduling, daily energy loss reduction could be reached up to 25% under an improved voltage profile that indicates more uniform loading for the DN.

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