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Title: Chapter 16 - 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.

Authors:
 [1];  [1];  [1];  [2];  [3]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
  2. University of Texas at Dallas
  3. Arizona State University
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
OSTI Identifier:
1426634
Report Number(s):
NREL/CH-5D00-71118
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Book
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; 29 ENERGY PLANNING, POLICY, AND ECONOMY; solar forecasting; wind forecasting; load forecasting; state estimation; machine learning

Citation Formats

Zhang, Yingchen, Yang, Rui, Hodge, Brian S, Zhang, Jie, and Weng, Yang. Chapter 16 - Predictive Analytics for Comprehensive Energy Systems State Estimation. United States: N. p., 2017. Web. doi:10.1016/B978-0-12-811968-6.00016-4.
Zhang, Yingchen, Yang, Rui, Hodge, Brian S, Zhang, Jie, & Weng, Yang. Chapter 16 - Predictive Analytics for Comprehensive Energy Systems State Estimation. United States. doi:10.1016/B978-0-12-811968-6.00016-4.
Zhang, Yingchen, Yang, Rui, Hodge, Brian S, Zhang, Jie, and Weng, Yang. Fri . "Chapter 16 - Predictive Analytics for Comprehensive Energy Systems State Estimation". United States. doi:10.1016/B978-0-12-811968-6.00016-4.
@article{osti_1426634,
title = {Chapter 16 - Predictive Analytics for Comprehensive Energy Systems State Estimation},
author = {Zhang, Yingchen and Yang, Rui and Hodge, Brian S and Zhang, Jie and Weng, Yang},
abstractNote = {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.},
doi = {10.1016/B978-0-12-811968-6.00016-4},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Dec 01 00:00:00 EST 2017},
month = {Fri Dec 01 00:00:00 EST 2017}
}

Book:
Other availability
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