Interval Load Forecasting for Individual Households in the Presence of
  Electric Vehicle Charging
Raiden Skala Mohamed Ahmed T. A. Elgalhud Katarina Grolinger Syed Mir
The transition to Electric Vehicles (EV) in place of traditional internal
combustion engines is increasing societal demand for electricity. The ability
to integrate the additional demand from EV charging into forecasting
electricity demand is critical for maintaining the reliability of electricity
generation and distribution. Load forecasting studies typically exclude
households with home EV charging, focusing on offices, schools, and public
charging stations. Moreover, they provide point forecasts which do not offer
information about prediction uncertainty. Consequently, this paper proposes the
Long Short-Term Memory Bayesian Neural Networks (LSTM-BNNs) for household load
forecasting in presence of EV charging. The approach takes advantage of the
LSTM model to capture the time dependencies and uses the dropout layer with
Bayesian inference to generate prediction intervals. Results show that the
proposed LSTM-BNNs achieve accuracy similar to point forecasts with the
advantage of prediction intervals. Moreover, the impact of lockdowns related to
the COVID-19 pandemic on the load forecasting model is examined, and the
analysis shows that there is no major change in the model performance as, for
the considered households, the randomness of the EV charging outweighs the
change due to pandemic.