Poisoning Attack on Load Forecasting

Yi Liang, Di He, Deming Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Short-term load forecasting systems for power grids have demonstrated high accuracy and have been widely employed for commercial use. However, classic load forecasting systems, which are based on statistical methods, are subject to vulnerability from training data poisoning. In this paper, we demonstrate a data poisoning strategy that effectively corrupts the forecasting model even in the presence of outlier detection. To the best of our knowledge, poisoning attack on short-term load forecasting with outlier detection has not been studied in previous works. Our method applies to several forecasting models, including the most widely-adapted and best-performing ones, such as multiple linear regression (MLR) and neural network (NN) models. Starting with the MLR model, we develop a novel closed-form solution to quickly estimate the new MLR model after a round of data poisoning without retraining. We then employ line search and simulated annealing to find the poisoning attack solution. Furthermore, we use the MLR attacking solution to generate a numerical solution for other models, such as NN. The effectiveness of our algorithm has been tested on the Global Energy Forecasting Competition (GEFCom2012) data set with the presence of outlier detection.

Original languageEnglish (US)
Title of host publication2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781728135205
StatePublished - May 2019
Event2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019 - Chengdu, China
Duration: May 21 2019May 24 2019

Publication series

Name2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019


Conference2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019


  • Load Forecasting
  • Multiple Linear Regression
  • Neural Network
  • Poisoning Attack

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering
  • Control and Optimization


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