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.

翻译：电动汽车取代传统内燃机汽车的进程正日益增加社会对电力的需求。将电动汽车充电带来的额外需求整合到电力需求预测中，对于维持发电和配电的可靠性至关重要。负荷预测研究通常排除拥有家庭充电桩的住户，而聚焦于办公楼、学校和公共充电站。此外，现有研究仅提供点预测，无法反映预测的不确定性。为此，本文提出长短期记忆贝叶斯神经网络（LSTM-BNNs），用于含电动汽车充电的家庭负荷预测。该方法利用LSTM模型捕捉时间依赖性，并通过基于贝叶斯推断的Dropout层生成预测区间。结果表明，所提出的LSTM-BNNs在保持与点预测相似精度的同时，额外提供预测区间的优势。此外，本文还考察了与COVID-19疫情相关的封锁措施对负荷预测模型的影响，分析显示模型性能未发生显著变化——对于所研究的家庭而言，电动汽车充电的随机性远超疫情带来的变化。