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The transition to Electric Vehicles (EV) in place of traditional internalcombustion engines is increasing societal demand for electricity. The abilityto integrate the additional demand from EV charging into forecastingelectricity demand is critical for maintaining the reliability of electricitygeneration and distribution. Load forecasting studies typically excludehouseholds with home EV charging, focusing on offices, schools, and publiccharging stations. Moreover, they provide point forecasts which do not offerinformation about prediction uncertainty. Consequently, this paper proposes theLong Short-Term Memory Bayesian Neural Networks (LSTM-BNNs) for household loadforecasting in presence of EV charging. The approach takes advantage of theLSTM model to capture the time dependencies and uses the dropout layer withBayesian inference to generate prediction intervals. Results show that theproposed LSTM-BNNs achieve accuracy similar to point forecasts with theadvantage of prediction intervals. Moreover, the impact of lockdowns related tothe COVID-19 pandemic on the load forecasting model is examined, and theanalysis shows that there is no major change in the model performance as, forthe considered households, the randomness of the EV charging outweighs thechange due to pandemic.

Interval Load Forecasting for Individual Households in the Presence of Electric Vehicle Charging