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

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 - Nov 27 2017

Fingerprint

State estimation
Electric power systems
Energy storage
Solar energy
Wind power
Learning systems
Sustainable development
Predictive analytics
Big data
Hot Temperature

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. (2017). 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

Predictive Analytics for Comprehensive Energy Systems State Estimation. / Zhang, Yingchen; Yang, Rui; Zhang, Jie; Weng, Yang; Hodge, Bri Mathias.

Big Data Application in Power Systems. Elsevier, 2017. p. 343-376.

Research output: Chapter in Book/Report/Conference proceedingChapter

Zhang, Y, Yang, R, Zhang, J, Weng, Y & Hodge, BM 2017, Predictive Analytics for Comprehensive Energy Systems State Estimation. in Big Data Application in Power Systems. Elsevier, pp. 343-376. https://doi.org/10.1016/B978-0-12-811968-6.00016-4
Zhang Y, Yang R, Zhang J, Weng Y, Hodge BM. Predictive Analytics for Comprehensive Energy Systems State Estimation. In Big Data Application in Power Systems. Elsevier. 2017. p. 343-376 https://doi.org/10.1016/B978-0-12-811968-6.00016-4
Zhang, Yingchen ; Yang, Rui ; Zhang, Jie ; Weng, Yang ; Hodge, Bri Mathias. / Predictive Analytics for Comprehensive Energy Systems State Estimation. Big Data Application in Power Systems. Elsevier, 2017. pp. 343-376
@inbook{4b861500121845fa97e7ce5788c296ee,
title = "Predictive Analytics for Comprehensive Energy Systems State Estimation",
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.",
keywords = "Load forecasting, Machine learning, Solar forecasting, State estimation, Wind forecasting",
author = "Yingchen Zhang and Rui Yang and Jie Zhang and Yang Weng and Hodge, {Bri Mathias}",
year = "2017",
month = "11",
day = "27",
doi = "10.1016/B978-0-12-811968-6.00016-4",
language = "English (US)",
isbn = "9780128119686",
pages = "343--376",
booktitle = "Big Data Application in Power Systems",
publisher = "Elsevier",

}

TY - CHAP

T1 - Predictive Analytics for Comprehensive Energy Systems State Estimation

AU - Zhang, Yingchen

AU - Yang, Rui

AU - Zhang, Jie

AU - Weng, Yang

AU - Hodge, Bri Mathias

PY - 2017/11/27

Y1 - 2017/11/27

N2 - 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.

AB - 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.

KW - Load forecasting

KW - Machine learning

KW - Solar forecasting

KW - State estimation

KW - Wind forecasting

UR - http://www.scopus.com/inward/record.url?scp=85042282679&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85042282679&partnerID=8YFLogxK

U2 - 10.1016/B978-0-12-811968-6.00016-4

DO - 10.1016/B978-0-12-811968-6.00016-4

M3 - Chapter

AN - SCOPUS:85042282679

SN - 9780128119686

SP - 343

EP - 376

BT - Big Data Application in Power Systems

PB - Elsevier

ER -