Chapter 16 - Predictive Analytics for Comprehensive Energy Systems State Estimation

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

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

Chapter 16 - Predictive Analytics for Comprehensive Energy Systems State Estimation

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

Power Systems Engineering

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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	American English
Title of host publication	Big Data Application in Power Systems
Editors	R. Arghandeh, Y. Zhou
Publisher	Elsevier
Pages	343-376
Number of pages	34
ISBN (Electronic)	9780128119686
ISBN (Print)	9780128119693
DOIs	https://doi.org/10.1016/B978-0-12-811968-6.00016-4 https://doi.org/10.1016/B978-0-12-811968-6.00016-4
State	Published - 2017

Bibliographical note

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

NREL Publication Number

NREL/CH-5D00-71118

Keywords

Load forecasting
Machine learning
Solar forecasting
State estimation
Wind forecasting

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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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AU - Zhang, Yingchen

AU - Yang, Rui

AU - Hodge, Brian

AU - Zhang, Jie

AU - Weng, Yang

PY - 2017

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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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SN - 9780128119693

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BT - Big Data Application in Power Systems

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A2 - Zhou, Y.

PB - Elsevier

ER -

Chapter 16 - Predictive Analytics for Comprehensive Energy Systems State Estimation

Abstract

Bibliographical note

NREL Publication Number

Keywords

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