Chapter 7: Predictive Analytics in Future Power Systems: A Panorama and State-of-the-Art of Deep Learning Applications

Sakshi Mishra, Andrew Glaws, Praveen Palanisamy

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

4 Scopus Citations

Abstract

The challenges surrounding the optimal operation of power systems are growing in various dimensions, due in part to increasingly distributed energy resources and a progression towards large-scale transportation electrification. Currently, the increasing uncertainties associated with both renewable energy generation and demand are largely being managed by increasing operational reserves—potentially at the cost of suboptimal economic conditions—in order to maintain the reliability of the system. This chapter looks at the big picture role of forecasting in power systems from generation to consumption and provides a comprehensive review of traditional approaches for forecasting generation and load in various contexts. This chapter then takes a deep dive into the state-of-the-art machine learning and deep learning approaches for power systems forecasting. Furthermore, a case study of multi-time-horizon solar irradiance forecasting using deep learning is discussed in detail. Smart grids form the backbone of the future interdependent networks. For addressing the challenges associated with the operations of smart grid, development and wide adoption of machine learning and deep learning algorithms capable of producing better forecasting accuracies is urgently needed. Along with exploring the implementation and benefits of these approaches, this chapter also considers the strengths and limitations of deep learning algorithms for power systems forecasting applications. This chapter, thus, provides a panoramic view of state-of-the-art of predictive analytics in power systems in the context of future smart grid operations.

Original languageAmerican English
Title of host publicationOptimization, Learning, and Control for Interdependent Complex Networks
Subtitle of host publicationAdvances in Intelligent Systems and Computing, Volume 1123
EditorsM. H. Amini
PublisherSpringer
Pages147-182
Number of pages36
DOIs
StatePublished - 2020

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1123
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2020.

NREL Publication Number

  • NREL/CH-7A40-74442

Keywords

  • Deep learning
  • Energy forecast
  • Machine learning
  • Power systems
  • Predictive analytic
  • Smart grid
  • Time series

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