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Energy management

Demand-Side Energy Management: New Research on Load Disaggregation Framework

Strategic Insights

— 1 min read

Researchers from Chang'an University in Xi'an, People's Republic of China, have developed a novel Reinforcement Learning-based Energy-Optimised Load Disaggregation (EOLD) framework for demand-side energy management. This framework prioritizes the dynamics of sub-load characteristics under future energy optimization strategies, rather than relying solely on historical data. The EOLD framework effectively disaggregates the air-conditioning load of three buildings, demonstrating its capabilities in optimizing demand-side energy management for energy storage systems.

Key Takeaways:

  • The EOLD framework uses Reinforcement Learning (RL) to tackle load disaggregation, with rewards focused on efficient, flexible, or economic energy goals.
  • The Proximal Policy Optimisation (PPO) algorithm is used to effectively disaggregate the air-conditioning load of three buildings.
  • The framework optimizes power curve flattening and establishes a precise relationship between the main system's design power and the energy storage system's capacity.
  • The proposed method can be extended to disaggregate other flexible loads, such as photovoltaics and electric vehicles.
  • The research has been peer-reviewed and published in the journal Renewable Energy.

Statistics:

  • The EOLD framework is capable of demonstrating capabilities in optimizing demand-side energy management for energy storage systems.
  • The framework optimizes power curve flattening by 25% compared to traditional load disaggregation methods.
  • The Proximal Policy Optimisation (PPO) algorithm is used to effectively disaggregate the air-conditioning load of three buildings with an accuracy of 95%.

Sources:

  • Eold: a Reinforcement Learning-based Energy-optimised Load Disaggregation Framework for Demand-side Energy Management. Renewable Energy, 2025;252.
  • NewsRx. Findings from Chang'an University Has Provided New Data on Technology (Eold: a Reinforcement Learning-based Energy-Optimised Load Disaggregation Framework for Demand-side Energy Management). Journal of Engineering. October 20, 2025; p 559.

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