As the transition to electric mobility is accelerating, EV fleet charging loads are expected to become increasingly significant for power systems. Hence, EV fleet load forecasting is vital to maintaining the reliability and safe operation of the power system. This paper presents a new multiple decomposition based hybrid forecasting model for EV fleet charging. The proposed approach incorporates the Swarm Decomposition (SWD) into the Complete Ensemble Empirical Mode Decomposition Adaptive Noise (CEEMDAN) method. The multiple decomposition approach offers more stable, stationary, and regular features of the original signals. Each decomposed signal is fed into artificial intelligence based forecasting models including multi-layer perceptron (MLP), long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM). Real EV fleet charging data sets from the field are used to validate the performance of the models. Various statistical metrics are used to quantify the prediction performance of the proposed model through a comparative analysis of the implemented models. It is demonstrated that the multiple decomposition approach improved the model performance with an R² value increasing from 0.8564 to 0.9766 as compared to the models with single decomposition.

Electric vehicles (EVs) hold promise for attaining sustainable development objectives and mitigating the effects of global climate change due to their substantial benefits, such as high energy efficiency and low carbon emissions. Research on purchase intentions and behaviors may accelerate the adoption of EVs. Considering that the number of studies on EVs increases in tandem with the size of the market and that mutual interaction supports this two-way growth, conducting studies on consumer behavior in this area in countries where the electric vehicle market is developing, such as Turkey, will provide valuable insights for both the industry and the government. In this study, published articles on the consumer behavior of current and potential purchasers of electric vehicles were analyzed on the axis of Turkey, and the trend of academic studies in the literature was systematized from a holistic standpoint. Co-citation, co-keyword, geographical, and thematic analysis were applied to articles about EV consumer behaviors published between 2004 and 2022 in journals indexed by WoS, Scopus, TR Index, and DergiPark. The results of this study can inform numerous inter-disciplinary studies and researchers on the consumer behavior of electric vehicles. The bibliometric analysis of academic studies on electric vehicle consumers not only closes the market’s knowledge gap and accelerates the adoption process by increasing consumer awareness, but also provides industry representatives and policymakers with insights for the expansion of the EV market.

Accurate predictions of solar photovoltaic (PV) power generation at different time horizons are essential for reliable operation of energy management systems. The output power of a PV power plant is dependent on non-linear and intermittent environmental factors, such as solar irradiance, wind speed, relative humidity, etc. Intermittency and randomness of solar PV power effect precision of estimation. To address the challenge, this paper presents a Swarm Decomposition Technique (SWD) based hybrid model as a novel approach for very short-term (15 min) solar PV power generation forecast. The original contribution of the study is to investigate use of SWD for solar data forecast. The solar PV power generation data with hourly resolution obtained from the field (grid connected, 857.08 kWp Akgul Solar PV Power Plant in Turkey) are used to develop and validate the forecast model. Specifically, the analysis showed that the hybrid model with SWD technique provides highly accurate predictions in cloudy periods.

With the increasing penetration of grid‐scale wind energy systems, accurate wind power forecasting is critical to optimizing their integration into the power system, ensuring operational reliability, and enabling efficient system asset utilization. Addressing this challenge, this study proposes a novel forecasting model that combines the long‐short‐term memory (LSTM) neural network with two signal decomposition techniques. The EMD technique effectively extracts stable, stationary, and regular patterns from the original wind power signal, while the VMD technique tackles the most challenging high‐frequency component. A deep learning‐based forecasting model, i.e. the LSTM neural network, is used to take advantage of its ability to learn from longer sequences of data and its robustness to noise and outliers. The developed model is evaluated against LSTM models employing various decomposition methods using real wind power data from three distinct offshore wind farms. It is shown that the two‐stage decomposition significantly enhances forecasting accuracy, with the proposed model achieving values up to 9.5% higher than those obtained using standard LSTM models.