This research work proposes an Improved Supervised Learning (ISL)-based Deep Neural
Network (DNN) for accurately forecasting the load demand of Electric Vehicles (EVs). This …

With an increase in Electric Vehicles (EV) that have unique charging requirements, finding
suitable Electric Vehicle Supply Equipment (EVSE) for charging EVs is becoming a difficult …

Large-scale electric vehicle (EV) charging scheduling is highly relevant for the growing
number of EVs, while it can be complex to solve. A few existing studies have applied a two …

Decarbonization of the transportation sector relies on the widespread adoption of Electric
Vehicles (EVs) and appropriate charging strategies. However, uncoordinated EV charging …

The emergence of electric vehicles is reshaping the energy landscape, requiring the
development of innovative energy integration mechanisms to engage prosumers. However …

The increasing scale of electric vehicles (EVs) and their stochastic charging behavior have
resulted in a growing coupling between the transportation network and the grid …

Exploring the convergence of electric vehicles (EVs), renewable energy, and smart grid
technologies in the context of Texas, this study addresses challenges hindering the …

Uncontrolled and uncoordinated charging of Electric Vehicles (EVs) is producing various
challenges in the distribution system, such as network congestion, overloading, and poor …

RNA sequence properties prediction is significant for understanding RNA function and its
potential applications in medicine and biotechnology. In this study, we developed a novel …

In this paper, we evaluate and analyze the performance of long short-term memory networks
(LSTMs) for individual electric vehicle (EV) charging behavior prediction over the next day …