Data-Driven Generation of Synthetic Load Datasets Preserving Spatio-Temporal Features
By Andrea Pinceti, Oliver Kosut, Lalitha Sankar. 2019
23 citations recorded by the source corpus.
A generative model for the creation of realistic historical bus-level load data for transmission grid models is presented. A data-driven approach based on principal component analysis is used to learn the spatio-temporal correlation between the loads in a system and build a generative model. Given a system topology and a set of base case loads, individual, realistic time-series data for each load can be generated. This technique is demonstrated by learning from a large proprietary dataset and generating historical data for the 2383-bus Polish test case.
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Abhishek Singh
- Praneeth Vepakomma
- Ramesh Raskar
- Fatemehsadat Mirshghallah
- Hadi Esmaeilzadeh
- Mohammadkazem Taram
- Vivek Sharma
- Chaoyang He
Citations in the corpus
- Privacy in Deep Learning: A Survey (2020, background): “There is a body of work studying information-theoretic based methods for both privacy and fairness, where privacy and fairness are provided through information degradation, through obfuscation or adversarial learning and demonstrated by mutual information reduction [67, 68, 48, 69, 70, 71, 72, 73, 7”