Online Testbed for Evaluating Vulnerability of Deep Learning Based Power Grid Load Forecasters

Published

July 7, 2020

Author(s)

Himanshu Neema, Peter Volgyesi, Xenofon Koutsoukos, Thomas Roth, Cuong Nguyen

Abstract

Modern electric grids that integrate smart grid technologies require different approaches to grid operations. There has been a shift towards increased reliance on distributed sensors to monitor bidirectional power flows and machine learning based load forecasting methods (e.g., using deep learning). These methods are fairly accurate under normal circumstances, but become highly vulnerable to stealthy adversarial attacks that could be deployed on the load forecasters. This paper provides a novel model-based Testbed for Simulation-based Evaluation of Resilience (TeSER) that enables evaluating deep learning based load forecasters against stealthy adversarial attacks. The testbed leverages three existing technologies, viz. DeepForge: for designing neural networks and machine learning pipelines, GridLAB-D: for electric grid distribution system simulation, and WebGME: for creating web-based collaborative metamodeling environments. The testbed architecture is described, and a case study to demonstrate its capabilities for evaluating load forecasters is provided.

Proceedings Title

Modeling and Simulation of Cyber-Physical Energy Systems

Conference Dates

April 21, 2020

Conference Location

Sydney, AU

Pub Type

Conferences

Download Paper

https://doi.org/10.1109/MSCPES49613.2020.9133701

Local Download

Keywords

power grid, load forecasting, machine learning, security, resilience, adversarial attacks, model- based testbed

Citation

Neema, H. , Volgyesi, P. , Koutsoukos, X. , Roth, T. and Nguyen, C. (2020), Online Testbed for Evaluating Vulnerability of Deep Learning Based Power Grid Load Forecasters, Modeling and Simulation of Cyber-Physical Energy Systems, Sydney, AU, [online], https://doi.org/10.1109/MSCPES49613.2020.9133701, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=930212 (Accessed October 26, 2025)