Chapter 16 - Predictive Analytics for Comprehensive Energy Systems State Estimation

Yingchen Zhang, Rui Yang, Brian Hodge, Jie Zhang, Yang Weng

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

12 Scopus Citations


Energy sustainability is a subject of concern to many nations in the modern world. It is critical for electric power systems to diversify energy supply to include systems with different physical characteristics, such as wind energy, solar energy, electrochemical energy storage, thermal storage, bio-energy systems, geothermal, and ocean energy. Each system has its own range of control variables and targets. To be able to operate such a complex energy system, big-data analytics become critical to achieve the goal of predicting energy supplies and consumption patterns, assessing system operation conditions, and estimating system states—all providing situational awareness to power system operators. This chapter presents data analytics and machine learning-based approaches to enable predictive situational awareness of the power systems.

Original languageAmerican English
Title of host publicationBig Data Application in Power Systems
EditorsR. Arghandeh, Y. Zhou
Number of pages34
ISBN (Electronic)9780128119686
ISBN (Print)9780128119693
StatePublished - 2017

Bibliographical note

Publisher Copyright:
© 2018 Elsevier Inc. All rights reserved.

NREL Publication Number

  • NREL/CH-5D00-71118


  • Load forecasting
  • Machine learning
  • Solar forecasting
  • State estimation
  • Wind forecasting


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