The paper "Forecasting smart home electricity consumption using VMD-Bi-GRU" by Ismael Jrhilifa, Hamid Ouadi, Abdelilah Jibab, and Nada Mounir addresses the challenge of accurately forecasting short-term household electricity consumption. The authors propose a hybrid model that combines variational mode decomposition (VMD) and bidirectional gated recurrent units (Bi-GRU) to predict 24 hours of household energy consumption with a 15-minute time granularity. VMD decomposes the power consumption time series into intrinsic mode functions (IMFs), and each IMF is predicted separately using Bi-GRU. The final prediction is obtained by summing the predictions of each model. The results show that the VMD-Bi-GRU model outperforms other models, achieving a mean squared error of 0.0038 KW, a mean absolute error of 0.046 KW, a mean absolute percentage error of 0.11%, and an R² score of 0.98. The study highlights the importance of accurate energy consumption forecasting in smart grids, power system management, and smart buildings, emphasizing the model's precision and effectiveness in handling the nonlinear and non-smooth characteristics of residential building electricity consumption data.The paper "Forecasting smart home electricity consumption using VMD-Bi-GRU" by Ismael Jrhilifa, Hamid Ouadi, Abdelilah Jibab, and Nada Mounir addresses the challenge of accurately forecasting short-term household electricity consumption. The authors propose a hybrid model that combines variational mode decomposition (VMD) and bidirectional gated recurrent units (Bi-GRU) to predict 24 hours of household energy consumption with a 15-minute time granularity. VMD decomposes the power consumption time series into intrinsic mode functions (IMFs), and each IMF is predicted separately using Bi-GRU. The final prediction is obtained by summing the predictions of each model. The results show that the VMD-Bi-GRU model outperforms other models, achieving a mean squared error of 0.0038 KW, a mean absolute error of 0.046 KW, a mean absolute percentage error of 0.11%, and an R² score of 0.98. The study highlights the importance of accurate energy consumption forecasting in smart grids, power system management, and smart buildings, emphasizing the model's precision and effectiveness in handling the nonlinear and non-smooth characteristics of residential building electricity consumption data.