The large scale deployment of Electric Vehicle (EV) charging infrastructure can result in high added utility costs due to the peak demand cost structure in utility bills for commercial and industrial users. A method to minimize this disincentive to EV adoption is proposed that relies on forecasting demand so that EV charging activity can be intelligently controlled. This study examines multiple forecasting models and techniques to determine the optimal algorithm for use in the proposed control system. Simulation results are presented for each of the forecasting algorithms with the best mean absolute percent error of 1.26% using a neural network with averaging. This results in a reduction in the peak demand electric costs of approximately 95%.

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Defense field as an engineer, project manager, and researcher. His specialization was in mechanical design, research and development, and business development. He studied at Murray State University and the University of Alabama at Birmingham where his research was on immersive virtual learning environments for educational training purposes. Furthermore, Dr. Webster has received various professional certifications from the American Society of Mechanical Engineers, SolidWorks Corporation, the Project Management Institute, and NACE International.

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