Article: A novel residential electricity load prediction algorithm based on hybrid seasonal decomposition and deep learning models Journal: International Journal of Energy Technology and Policy (IJETP) 2025 Vol.20 No.5 pp.1 - 23 Abstract: Residential electricity load prediction is of great significance for power system planning. With the increasing complexity and uncertainty of the power grid, traditional prediction models still have insufficient accuracy and neglect seasonal changes. In this paper, a data-driven multi-scale hybrid prediction model for residential electricity load is proposed, which integrates a convolutional neural network (CNN), long short-term memory (LSTM), and attention mechanism. The seasonal decomposition was applied to extract seasonal patterns of the electricity consumption data. The hybrid model integrates the parallel processing capability of CNN and the long time-series modelling capability of LSTM to capture the spatial-temporal characteristics of electricity load accurately. The attention mechanism is employed to calculate the critical weight to enhance the prediction accuracy dynamically. Finally, detailed comparison experiments show that the proposed hybrid model outperformed state-of-the-art algorithms. The MAPE of the hourly and daily prediction results of the proposed model are 2.36% and 0.76%, respectively. Inderscience Publishers - linking academia, business and industry through research

Title: A novel residential electricity load prediction algorithm based on hybrid seasonal decomposition and deep learning models

Authors: Shan Gao; Xinran Zhang; Lihong Gao; Yancong Zhou

Addresses: School of Information and Engineering, Tianjin University of Commerce, Tianjin, 300134, China ' School of Information and Engineering, Tianjin University of Commerce, Tianjin, 300134, China ' School of Information and Engineering, Tianjin University of Commerce, Tianjin, 300134, China ' School of Information and Engineering, Tianjin University of Commerce, Tianjin, 300134, China

Abstract: Residential electricity load prediction is of great significance for power system planning. With the increasing complexity and uncertainty of the power grid, traditional prediction models still have insufficient accuracy and neglect seasonal changes. In this paper, a data-driven multi-scale hybrid prediction model for residential electricity load is proposed, which integrates a convolutional neural network (CNN), long short-term memory (LSTM), and attention mechanism. The seasonal decomposition was applied to extract seasonal patterns of the electricity consumption data. The hybrid model integrates the parallel processing capability of CNN and the long time-series modelling capability of LSTM to capture the spatial-temporal characteristics of electricity load accurately. The attention mechanism is employed to calculate the critical weight to enhance the prediction accuracy dynamically. Finally, detailed comparison experiments show that the proposed hybrid model outperformed state-of-the-art algorithms. The MAPE of the hourly and daily prediction results of the proposed model are 2.36% and 0.76%, respectively.

Keywords: electricity consumption prediction; deep learning; convolutional neural network; CNN; long short-term memory; LSTM; attention mechanism.

DOI: 10.1504/IJETP.2025.146888

International Journal of Energy Technology and Policy, 2025 Vol.20 No.5, pp.1 - 23

Received: 29 Jan 2025
Accepted: 08 May 2025
Published online: 24 Jun 2025 *

Title: A novel residential electricity load prediction algorithm based on hybrid seasonal decomposition and deep learning models

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