Predictive Analytics for Comprehensive Energy Systems State Estimation

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

doi:10.1016/B978-0-12-811968-6.00016-4

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 proceeding › Chapter

12 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 language	English (US)
Title of host publication	Big Data Application in Power Systems
Publisher	Elsevier
Pages	343-376
Number of pages	34
ISBN (Electronic)	9780128119693
ISBN (Print)	9780128119686
DOIs	https://doi.org/10.1016/B978-0-12-811968-6.00016-4
State	Published - 2018

Keywords

Load forecasting
Machine learning
Solar forecasting
State estimation
Wind forecasting

ASJC Scopus subject areas

General Energy

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

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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.",

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author = "Yingchen Zhang and Rui Yang and Jie Zhang and Yang Weng and Hodge, {Bri Mathias}",

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doi = "10.1016/B978-0-12-811968-6.00016-4",

language = "English (US)",

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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 - 2018

Y1 - 2018

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

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M3 - Chapter

AN - SCOPUS:85042282679

SN - 9780128119686

SP - 343

EP - 376

BT - Big Data Application in Power Systems

PB - Elsevier

ER -

Predictive Analytics for Comprehensive Energy Systems State Estimation

Abstract

Keywords

ASJC Scopus subject areas

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