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Title: Optimization Based Data Mining Approah for Forecasting Real-Time Energy Demand

Abstract

The worldwide concern over environmental degradation, increasing pressure on electric utility companies to meet peak energy demand, and the requirement to avoid purchasing power from the real-time energy market are motivating the utility companies to explore new approaches for forecasting energy demand. Until now, most approaches for forecasting energy demand rely on monthly electrical consumption data. The emergence of smart meters data is changing the data space for electric utility companies, and creating opportunities for utility companies to collect and analyze energy consumption data at a much finer temporal resolution of at least 15-minutes interval. While the data granularity provided by smart meters is important, there are still other challenges in forecasting energy demand; these challenges include lack of information about appliances usage and occupants behavior. Consequently, in this paper, we develop an optimization based data mining approach for forecasting real-time energy demand using smart meters data. The objective of our approach is to develop a robust estimation of energy demand without access to these other building and behavior data. Specifically, the forecasting problem is formulated as a quadratic programming problem and solved using the so-called support vector machine (SVM) technique in an online setting. The parameters of the SVMmore » technique are optimized using simulated annealing approach. The proposed approach is applied to hourly smart meters data for several residential customers over several days.« less

Authors:
 [1];  [2];  [2]
  1. ORNL
  2. University of Tennessee, Knoxville (UTK)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1185951
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: 2015 Industrial and Systems Engineering Research Conference, Nashville, TN, USA, 20150530, 20150602
Country of Publication:
United States
Language:
English
Subject:
Energy management; energy efficiency; data mining; optimization; simulated annealing

Citation Formats

Omitaomu, Olufemi A, Li, Xueping, and Zhou, Shengchao. Optimization Based Data Mining Approah for Forecasting Real-Time Energy Demand. United States: N. p., 2015. Web.
Omitaomu, Olufemi A, Li, Xueping, & Zhou, Shengchao. Optimization Based Data Mining Approah for Forecasting Real-Time Energy Demand. United States.
Omitaomu, Olufemi A, Li, Xueping, and Zhou, Shengchao. 2015. "Optimization Based Data Mining Approah for Forecasting Real-Time Energy Demand". United States.
@article{osti_1185951,
title = {Optimization Based Data Mining Approah for Forecasting Real-Time Energy Demand},
author = {Omitaomu, Olufemi A and Li, Xueping and Zhou, Shengchao},
abstractNote = {The worldwide concern over environmental degradation, increasing pressure on electric utility companies to meet peak energy demand, and the requirement to avoid purchasing power from the real-time energy market are motivating the utility companies to explore new approaches for forecasting energy demand. Until now, most approaches for forecasting energy demand rely on monthly electrical consumption data. The emergence of smart meters data is changing the data space for electric utility companies, and creating opportunities for utility companies to collect and analyze energy consumption data at a much finer temporal resolution of at least 15-minutes interval. While the data granularity provided by smart meters is important, there are still other challenges in forecasting energy demand; these challenges include lack of information about appliances usage and occupants behavior. Consequently, in this paper, we develop an optimization based data mining approach for forecasting real-time energy demand using smart meters data. The objective of our approach is to develop a robust estimation of energy demand without access to these other building and behavior data. Specifically, the forecasting problem is formulated as a quadratic programming problem and solved using the so-called support vector machine (SVM) technique in an online setting. The parameters of the SVM technique are optimized using simulated annealing approach. The proposed approach is applied to hourly smart meters data for several residential customers over several days.},
doi = {},
url = {https://www.osti.gov/biblio/1185951}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Jan 01 00:00:00 EST 2015},
month = {Thu Jan 01 00:00:00 EST 2015}
}

Conference:
Other availability
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