Citation: Skala, R.; Elgalhud, M.A.T.A.; Grolinger, K.; Mir, S. Interval Load Forecasting for Individual Households in the Presence of Electric Vehicle Charging. Energies 2023, 16, 4093. https:// doi.org/10.3390/en16104093 Academic Editors: Antonio Gabaldón, María Carmen Ruiz-Abellón and Luis Alfredo Fernández-Jiménez Received: 20 March 2023 Revised: 8 May 2023 Accepted: 10 May 2023 Published: 15 May 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). energies Article Interval Load Forecasting for Individual Households in the Presence of Electric Vehicle Charging Raiden Skala 1 , Mohamed Ahmed T. A. Elgalhud 1 , Katarina Grolinger 1, * and Syed Mir 2 1 Department of Electrical and Computer Engineering, Western University, London, ON N6A 5B9, Canada 2 London Hydro, London, ON N6A 4H6, Canada * Correspondence: kgroling@uwo.ca; Tel.: +1-519-661-2111 (ext. 81407) Abstract: 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. Keywords: residential load forecasting; Long Short-Term Memory; Bayesian Neural Network; Bayesian optimization; interval forecasting; EV charging; analysis of variance 1. Introduction Affordable and reliable sources of electricity enable the sustainable growth of strong economies and can improve the average person’s quality of life [1] by providing reliable access to appliances, medical equipment, communication, entertainment, and other devices. The dependence on power grids to provide electricity is increasing due to the continuous integration of novel electronic devices into every aspect of modern life [2] as these devices rely on a reliable source of electricity. The mainstream adoption of electric vehicles (EVs) away from traditional internal combustion engine (ICE) vehicles for consumer use is set further to entrench reliance on access to electricity due to the increased electricity demand for charging. While this transition can be a positive step in reducing carbon emissions [3], embracing EVs will shift transportation energy requirements from petroleum- based products to electric grids. Of specific interest to this paper, is the demand created by charging EVs in residential households, which is frequently utilized due to its relative affordability and convenience. As countries, such as Canada, plan to ban the sale of new ICE vehicles by 2035 [4], preparations are required to ensure the success of this shift. This includes actions such as installing charging stations, increasing electricity generation capacity, investing into battery technologies, and improving infrastructure throughout the grid to handle the higher loads required by EV charging. The capability of electricity distribution companies to accurately forecast the hourly electricity consumption of residential households that own EVs is instrumental in the transition to EVs as it assists the utility companies to Energies 2023, 16, 4093. https://doi.org/10.3390/en16104093 https://www.mdpi.com/journal/energies