ABSTRACT
Growing energy consumption has been a contemporary problem, especially in the climate crisis and the COVID-19 pandemic. Many statistical reports have stated that there is an increase in energy consumption from residential households to the industrial sector. Electricity consumption forecasting is extremely important as it supports power system decision-making and management. In this paper, traditional ARIMAX and SARIMAX forecasting models and RNN-based deep learning models were used to model the electricity consumption historical data of a two-storied house located in Houston, Texas, USA. The features used in the modeling process include the daily-average electricity consumption historical data of the two-storied house, day category (weekday, weekend, vacation day, and COVID-lockdown), and weather-related variables. Each model's respective error performance on the testing dataset is compared. The result showed that RNN-based deep learning models outperformed the traditional ARIMAX and SARIMAX models in forecasting the daily-average electricity consumption of the two-storied house and that the performance of the RNN-based deep learning models doesn't differ significantly from each other. © 2022 IEEE.