# Predictive Analytics in Future Power Systems: A Panorama and State-Of-The-Art of Deep Learning Applications

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## 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.

## Keywords

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

### Acknowledgments

The authors wish to thank Kate Anderson and Adam Warren (National Renewable Energy Laboratory) for the encouragement to pursue this research work. The authors would also like to thank Manajit Sengupta and Ryan King (National Renewable Energy Laboratory) for providing useful suggestions to refine the manuscript. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government.

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