Paper on Evaluating Performance of Different Generative Adversarial Networks for Large-Scale Building Power Demand Prediction has been published in Energy and Buildings
How could we predict large-scale building power demand fast and accurately? Generative Adversarial Networks (GAN), as a potential candidate, have recently attracted a lot of attention. This paper identified five promising GANs (Original GAN, cGAN, SGAN, InfoGAN, and ACGAN) and evaluates their performance for predicting building power demand at a large scale. As a result, cGAN and Original GAN are recommended.
This work has been published under the title 鈥淓valuating Performance of Different Generative Adversarial Networks for Large-Scale Building Power Demand Prediction鈥 in the journal Energy and Buildings. The full paper is available .
The first author of this paper, , is the former member of the SBS Lab. He is currently a Research Scientist at Pacific Northwest National Laboratory (PNNL). His research focuses on building energy modeling, building code and standards analyses, urban scale modeling, building-to-grid integration, and energy policies.
Congratulations to Yunyang on publishing this paper!