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Title: Consumption Behavior Analytics-Aided Energy Forecasting and Dispatch

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.

Authors:
 [1];  [1];  [1];  [2];  [3]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
  2. University of Illinois Urbana-Champaign
  3. University of Denver
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:
1379464
Report Number(s):
NREL/JA-5D00-70064
Journal ID: ISSN 1524-9050
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Journal Article
Resource Relation:
Journal Name: IEEE Transactions on Intelligent Transportation Systems; Journal Volume: 32; Journal Issue: 4
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; electricity consumption behavior; very-short-term energy forecast; machine learning; time series analysis; abnormal days; online forecast; predictive distribution energy management; intelligent systems; artificial intelligence

Citation Formats

Zhang, Yingchen, Yang, Rui, Jiang, Huaiguang, Zhang, Kaiqing, and Zhang, Jun Jason. Consumption Behavior Analytics-Aided Energy Forecasting and Dispatch. United States: N. p., 2017. Web. doi:10.1109/MIS.2017.3121551.
Zhang, Yingchen, Yang, Rui, Jiang, Huaiguang, Zhang, Kaiqing, & Zhang, Jun Jason. Consumption Behavior Analytics-Aided Energy Forecasting and Dispatch. United States. doi:10.1109/MIS.2017.3121551.
Zhang, Yingchen, Yang, Rui, Jiang, Huaiguang, Zhang, Kaiqing, and Zhang, Jun Jason. Thu . "Consumption Behavior Analytics-Aided Energy Forecasting and Dispatch". United States. doi:10.1109/MIS.2017.3121551.
@article{osti_1379464,
title = {Consumption Behavior Analytics-Aided Energy Forecasting and Dispatch},
author = {Zhang, Yingchen and Yang, Rui and Jiang, Huaiguang and Zhang, Kaiqing and Zhang, Jun Jason},
abstractNote = {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.},
doi = {10.1109/MIS.2017.3121551},
journal = {IEEE Transactions on Intelligent Transportation Systems},
number = 4,
volume = 32,
place = {United States},
year = {Thu Aug 17 00:00:00 EDT 2017},
month = {Thu Aug 17 00:00:00 EDT 2017}
}