Predictive Analytics for Comprehensive Energy Systems State Estimation

Yingchen Zhang, Rui Yang, Jie Zhang, Yang Weng, Bri Mathias Hodge

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Scopus citations

Abstract

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 languageEnglish (US)
Title of host publicationBig Data Application in Power Systems
PublisherElsevier
Pages343-376
Number of pages34
ISBN (Electronic)9780128119693
ISBN (Print)9780128119686
DOIs
StatePublished - Jan 1 2018

    Fingerprint

Keywords

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

ASJC Scopus subject areas

  • Energy(all)

Cite this

Zhang, Y., Yang, R., Zhang, J., Weng, Y., & Hodge, B. M. (2018). Predictive Analytics for Comprehensive Energy Systems State Estimation. In Big Data Application in Power Systems (pp. 343-376). Elsevier. https://doi.org/10.1016/B978-0-12-811968-6.00016-4