Grid-Constrained Data Cleansing Method for Enhanced Busload Forecasting

Zhenghui Li, Jiaming Liu, Yuzhang Lin, Fei Wang

Research output: Contribution to journalArticlepeer-review


A number of measurement data cleansing methods have been proposed aiming at improving the accuracy of busload forecasting (BLF). However, almost all these methods only exploit the temporal characteristics of a single measurement time series of busload data and neglect the information that can be extracted from the power grid. As a matter of fact, the power grid model derived from physical circuit laws can provide an entirely different dimension of information facilitating the cleansing of busload data. In view of this gap, a comprehensive data cleansing framework is proposed for the sake of enhancing BLF accuracy. Busload datasets are cleansed based on their consistencies with both the temporal statistics and the physical power grid model. Simulation results on the IEEE 30-bus system verify that the proposed method remarkably improves BLF accuracy under various types of measurement data corruption conditions, including random noise, temporary gross errors, permanent biases, and cyberattacks.

Original languageEnglish (US)
Article number9415698
JournalIEEE Transactions on Instrumentation and Measurement
StatePublished - 2021


  • Anomaly detection
  • Gaussian mixture model (GMM)
  • data cleansing
  • load forecasting
  • measurement error
  • state estimation (SE)

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering


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