Author
Listed:
- Siqi Liu
(School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China)
- Zhiyuan Xie
(School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China)
- Zhengwei Hu
(School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China)
- Kaisa Zhang
(School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China)
- Weidong Gao
(School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China)
- Xuewen Liu
(Department of Electronic and Communication Engineering, Beijing Electronic Science and Technology Institute, Beijing 100071, China)
AbstractWith the increasing integration of renewable energy and smart technologies in residential energy systems, proactive household energy management (HEM) have become critical for reducing costs, enhancing grid stability, and achieving sustainability goals. This study proposes a ultra-short-term forecasting-driven proactive energy consumption optimization strategy that integrates advanced forecasting models with multi-objective scheduling algorithms. By leveraging deep learning techniques like Graph Attention Network (GAT) architectures, the system predicts ultra-short-term household load profiles with high accuracy, addressing the volatility of residential energy use. Then, based on the predicted data, a comprehensive consideration of electricity costs, user comfort, carbon emission pricing, and grid load balance indicators is undertaken. This study proposes an enhanced mixed-integer optimization algorithm to collaboratively optimize multiple objective functions, thereby refining appliance scheduling, energy storage utilization, and grid interaction. Case studies demonstrate that integrating photovoltaic (PV) power generation forecasting and load forecasting models into a home energy management system, and adjusting the original power usage schedule based on predicted PV output and water heater demand, can effectively reduce electricity costs and carbon emissions without compromising user engagement in optimization. This approach helps promote energy-saving and low-carbon electricity consumption habits among users.
Suggested Citation
Siqi Liu & Zhiyuan Xie & Zhengwei Hu & Kaisa Zhang & Weidong Gao & Xuewen Liu, 2025.
"Ultra-Short-Term Forecasting-Based Optimization for Proactive Home Energy Management,"
Energies, MDPI, vol. 18(15), pages 1-29, July.
Handle:
RePEc:gam:jeners:v:18:y:2025:i:15:p:3936-:d:1708414
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