Consumption Behavior Analytics-Aided Energy Forecasting and Dispatch

Yingchen Zhang, Rui Yang, Huaiguang Jiang, Kaiqing Zhang, Jun Zhang

Research output: Contribution to journalArticlepeer-review

25 Scopus Citations

Abstract

For decades, electricity customers have been treated as mere recipients of electricity in vertically integrated power systems. However, as customers have widely adopted distributed energy resources and other forms of customer participation in active dispatch (such as demand response) have taken shape, the value of mining knowledge from customer behavior patterns and using it for power system operation is increasing. Further, the variability of renewable energy resources has been considered a liability to the grid. However, electricity consumption has shown the same level of variability and uncertainty, and this is sometimes overlooked. This article investigates data analytics and forecasting methods to identify correlations between electricity consumption behavior and distributed photovoltaic (PV) output. The forecasting results feed into a predictive energy management system that optimizes energy consumption in the near future to balance customer demand and power system needs.

Original languageAmerican English
Article number8012304
Pages (from-to)59-63
Number of pages5
JournalIEEE Intelligent Systems
Volume32
Issue number4
DOIs
StatePublished - 2017

Bibliographical note

Publisher Copyright:
© 2001-2011 IEEE.

NREL Publication Number

  • NREL/JA-5D00-70064

Keywords

  • abnormal days
  • artificial intelligence
  • electricity consumption behavior
  • intelligent systems
  • machine learning
  • online forecast
  • predictive distribution energy management
  • time series analysis
  • very-short-term energy forecast

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