Large scale power demand prediction for buildings plays a great role in stable operation and management for the grid. To predict large scale power demand in an accurate and fast way, our team has developed a new method called E-GAN, which combines a physics-based model (EnergyPlus) and a data-driven model (GAN), to predict the daily power demand for buildings at a large scale.
This work has been published under the title "Daily power demand prediction for buildings at a large scale using a hybrid of physics-based model and generative adversarial network " in the journal Building Simulation. The full paper is availbale .
The first author of this paper, Chenlu Tian, was听a visiting Ph.D. scholar in the SBS lab, where her research focused听on building data analysis using machine learning methods.
Congratulations to Chenlu on publishing this paper!
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