Year: 2025
Type: article
Abstract: Accurate forecasting of energy consumption is crucial for the efficient management and control of modern energy grids, particularly amid the escalating integration of renewable energy sources. This study proposes a hybrid approach that combines the Autoregressive Integrated Moving Average (ARIMA) and the Gated Recurrent Unit neural network (GRU) to predict energy consumption in a microgrid setting. The proposed hybrid ARIMA-GRU model integrates ARIMA’s residuals with GRU’s non-linear modeling capabilities, enabling enhanced prediction accuracy while capturing both linear and non-linear dependencies in microgrid energy data. The model’s performance is evaluated using real-world energy consumption data, achieving an RMSE of 38.28 kWh, MAE of 31.24 kWh, and MAPE of 10.29%. These results highlight the model’s effectiveness in improving energy forecasting and providing practical insights for better energy management in microgrids.
Authors: Lahoucine Oumiguil, Ali Nejmi
Institution: Université Sultan Moulay Slimane
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4414830809