Multi-step hourly electrical load forecasting for a single household: A hybrid deep learning approach based on CNN, BiGRU, and Attention Mechanism
Year: 2026
Type: article
Abstract: Accurate electrical load forecasting is crucial for the efficient management of modern power grids. However, the highly variable consumption patterns of single households present a significant challenge, especially for multi-step forecasting, which holds high practical value. This study addresses this challenge by proposing a novel hybrid deep learning model that combines Convolutional Neural Networks, a Bidirectional Gated Recurrent Unit, and an Attention mechanism. The proposed model was trained and tested to forecast the next hour in 6 steps, based on 24 hours of 10-minute interval data. The experimental results demonstrate that the proposed architecture, despite the complexity and granularity of the problem, achieves remarkably low error rates, with a Mean Absolute Error of 0.0393, a Mean Squared Error of 0.0044, and a Root Mean Squared Error of 0.0666. This performance is competitive with results obtained by many state-of-the-art models on single-step forecasting tasks. Consequently, this study makes a unique contribution to the field by presenting an effective and robust solution to the multi-step hourly forecasting problem, a topic less explored in the literature.
Institution: Kırıkkale University
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7138132912