In the quest to make buildings smarter and more energy-efficient, a groundbreaking study led by Bangpeng Xie from the State Grid Shanghai Pudong Electric Power Supply Company has introduced a novel framework that could revolutionize how we manage energy demand in buildings. Published in the journal *Buildings* (translated as “大楼” in English), this research offers a compelling solution to a pressing challenge: how to quantify and harness the energy flexibility of buildings to support power grids with high renewable energy penetration.
Buildings consume a staggering 32% of global energy, making them pivotal players in the energy landscape. However, their potential as flexible demand-side resources has remained largely untapped due to the lack of an integrated approach to quantify their energy-adjustable margin (BAM) and response duration (RD) under realistic operational constraints. Xie and his team have bridged this gap by developing a coupled data–physical simulation framework that integrates advanced load forecasting with detailed building simulation.
At the heart of this framework is a hybrid load forecasting model called PSO-LSTM-RF, which combines Particle Swarm Optimization, Long Short-Term Memory networks, and Random Forest algorithms. This model achieves remarkably high accuracy, with an average R² of 0.985 and mean absolute percentage errors ranging from 1.92% to 5.75%. “The accuracy of our load forecasting model is crucial because it forms the foundation for all subsequent analyses,” Xie explains. “Without precise predictions, we cannot reliably quantify the flexibility potential of buildings.”
The predicted load profiles are then mapped to physically consistent baseline and demand-response scenarios using a similar-day matching mechanism. This allows the researchers to jointly quantify BAM and RD under explicit thermal comfort constraints, ensuring that energy adjustments do not compromise occupant comfort.
The case studies conducted on offices, shopping malls, and hotels reveal significant heterogeneity in their flexibility profiles. Hotels, for instance, exhibit the largest BAM (up to 579.27 kWh) and the longest RD (up to 135 minutes), making them highly flexible resources. Shopping malls maintain stable high flexibility, while offices show moderate BAM with minimal operational disruption. “The heterogeneity among building types underscores the importance of tailored demand-response strategies,” notes Xie. “A one-size-fits-all approach simply won’t work.”
The implications of this research are far-reaching. By establishing a closed-loop link between data-driven prediction and physics-based simulation, the framework provides interpretable flexibility indicators that can support demand-response planning, virtual power plant aggregation, and coordinated optimization of source–grid–load interactions. “This framework offers a powerful tool for energy managers and grid operators to leverage the flexibility of buildings more effectively,” Xie says. “It’s a step towards creating a more resilient and sustainable energy ecosystem.”
As the energy sector continues to evolve, the ability to harness the flexibility of buildings will become increasingly important. This research not only advances our understanding of building energy management but also paves the way for innovative solutions that can enhance grid stability and support the integration of renewable energy sources. With the framework developed by Xie and his team, the future of energy management in buildings looks brighter and more flexible than ever before.