Plug-in Electric Vehicle Charging Demand Prediction Using Artificial Intelligence
Presenter Type
UNO Graduate Student (Doctoral)
Major/Field of Study
Architectural Engineering
Other
Architectural Engineering
Advisor Information
Mahmoud Alahmad
Location
MBSC Ballroom Poster # 309 - G (Doctoral)
Presentation Type
Poster
Start Date
24-3-2023 2:30 PM
End Date
24-3-2023 3:45 PM
Abstract
Climate change has been a serious issue around the world for a long time, and innumerable resolutions have been offered to decrease the issues caused by global warming . In the outcome of the Paris Agreement of 2015, each country was required to decrease the emission levels in a dynamic action to oppose climate change . Most countries started to reduce the emissions in their transportation division by encouraging people to use electric vehicles instead of conventional vehicles . Many challenges appear due to the variation in Plug-in Electric Vehicle (PEV) user charging behavior as well as battery sizes. Limited information is available about the effect of charging behavior on the distribution network and its reliability at public charging stations in any given area. Generally, monitoring the energy consumption has become one of the most important factors in green and micro grids; therefore.
The main objective of this research is to assess the feasibility of predicting the energy demand of a charging session, using only information available at the start of charging. This prediction could help to efficiently manage the electric grid. Consequently, machine-learning methods will be applied in this research to predict the charging demand for the PEV user after a charging session starts. This approach will be validated using a real data collected from different household charging stations installed in 480 homes in Omaha, NE, USA. The performance of different regression methods for predicting the charging demand will be characterized using established statistical metrics. Accurate prediction of session charging demand has many possible applications, including scheduling, grid stability, and smart grid integration.
Scheduling
2:30 -3:45 p.m.
Plug-in Electric Vehicle Charging Demand Prediction Using Artificial Intelligence
MBSC Ballroom Poster # 309 - G (Doctoral)
Climate change has been a serious issue around the world for a long time, and innumerable resolutions have been offered to decrease the issues caused by global warming . In the outcome of the Paris Agreement of 2015, each country was required to decrease the emission levels in a dynamic action to oppose climate change . Most countries started to reduce the emissions in their transportation division by encouraging people to use electric vehicles instead of conventional vehicles . Many challenges appear due to the variation in Plug-in Electric Vehicle (PEV) user charging behavior as well as battery sizes. Limited information is available about the effect of charging behavior on the distribution network and its reliability at public charging stations in any given area. Generally, monitoring the energy consumption has become one of the most important factors in green and micro grids; therefore.
The main objective of this research is to assess the feasibility of predicting the energy demand of a charging session, using only information available at the start of charging. This prediction could help to efficiently manage the electric grid. Consequently, machine-learning methods will be applied in this research to predict the charging demand for the PEV user after a charging session starts. This approach will be validated using a real data collected from different household charging stations installed in 480 homes in Omaha, NE, USA. The performance of different regression methods for predicting the charging demand will be characterized using established statistical metrics. Accurate prediction of session charging demand has many possible applications, including scheduling, grid stability, and smart grid integration.