@article{34738,
  keywords = {Machine learning, Load shape, Smart meter data, Electrical load profile, Generative Adversarial Network (GAN)},
  author = {Zhe Wang and Tianzhen Hong},
  title = {Generating realistic building electrical load profiles through the Generative Adversarial Network (GAN)},
  abstract = {<p>Building electrical load profiles can improve understanding of building energy efficiency, demand flexibility, and building-grid interactions. Current approaches to generating load profiles are time-consuming and not capable of reflecting the dynamic and stochastic behaviors of real buildings; some approaches also trigger data privacy concerns. In this study, we proposed a novel approach for generating realistic electrical load profiles of buildings through the Generative Adversarial Network (GAN), a machine learning technique that is capable of revealing an unknown probability distribution purely from data. The proposed approach has three main steps: (1) normalizing the daily 24-hour load profiles, (2) clustering the daily load profiles with the k-means algorithm, and (3) using GAN to generate daily load profiles for each cluster. The approach was tested with an open-source database – the Building Data Genome Project. We validated the proposed method by comparing the mean, standard deviation, and distribution of key parameters of the generated load profiles with those of the real ones. The KL divergence of the generated and real load profiles are within 0.3 for majority of parameters and clusters. Additionally, results showed the load profiles generated by GAN can capture not only the general trend but also the random variations of the actual electrical loads in buildings. The proposed GAN approach can be used to generate building electrical load profiles, verify other load profile generation models, detect changes to load profiles, and more importantly, anonymize smart meter data for sharing, to support research and applications of grid-interactive efficient buildings.</p>},
  year = {2020},
  journal = {Energy and Buildings},
  volume = {224},
  pages = {110299},
  month = {10/2020},
  issn = {03787788},
  doi = {10.1016/j.enbuild.2020.110299},
  language = {eng},
}