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Title: Predicting Future Hourly Residential Electrical Consumption: A Machine Learning Case Study

Abstract

Whole building input models for energy simulation programs are frequently created in order to evaluate specific energy savings potentials. They are also often utilized to maximize cost-effective retrofits for existing buildings as well as to estimate the impact of policy changes toward meeting energy savings goals. Traditional energy modeling suffers from several factors, including the large number of inputs required to characterize the building, the specificity required to accurately model building materials and components, simplifying assumptions made by underlying simulation algorithms, and the gap between the as-designed and as-built building. Prior works have attempted to mitigate these concerns by using sensor-based machine learning approaches to model energy consumption. However, a majority of these prior works focus only on commercial buildings. The works that focus on modeling residential buildings primarily predict monthly electrical consumption, while commercial models predict hourly consumption. This means there is not a clear indicator of which techniques best model residential consumption, since these methods are only evaluated using low-resolution data. We address this issue by testing seven different machine learning algorithms on a unique residential data set, which contains 140 different sensors measurements, collected every 15 minutes. In addition, we validate each learner's correctness on the ASHRAEmore » Great Energy Prediction Shootout, using the original competition metrics. Our validation results confirm existing conclusions that Neural Network-based methods perform best on commercial buildings. However, the results from testing our residential data set show that Feed Forward Neural Networks, Support Vector Regression (SVR), and Linear Regression methods perform poorly, and that Hierarchical Mixture of Experts (HME) with Least Squares Support Vector Machines (LS-SVM) performs best - a technique not previously applied to this domain.« less

Authors:
 [1];  [1];  [1]
  1. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
OSTI Identifier:
1041424
DOE Contract Number:  
DE-AC05-00OR22725
Resource Type:
Journal Article
Journal Name:
Energy and Buildings
Additional Journal Information:
Journal Volume: 49; Journal Issue: 0; Journal ID: ISSN 0378-7788
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; ALGORITHMS; BUILDING MATERIALS; COMMERCIAL BUILDINGS; ENERGY CONSUMPTION; FORECASTING; LEARNING; METRICS; MIXTURES; NEURAL NETWORKS; RESIDENTIAL BUILDINGS; SENSORS; SIMULATION; SPECIFICITY; TESTING; VALIDATION; VECTORS

Citation Formats

Edwards, Richard E, New, Joshua Ryan, and Parker, Lynne Edwards. Predicting Future Hourly Residential Electrical Consumption: A Machine Learning Case Study. United States: N. p., 2012. Web. doi:10.1016/j.enbuild.2012.03.010.
Edwards, Richard E, New, Joshua Ryan, & Parker, Lynne Edwards. Predicting Future Hourly Residential Electrical Consumption: A Machine Learning Case Study. United States. doi:10.1016/j.enbuild.2012.03.010.
Edwards, Richard E, New, Joshua Ryan, and Parker, Lynne Edwards. Sun . "Predicting Future Hourly Residential Electrical Consumption: A Machine Learning Case Study". United States. doi:10.1016/j.enbuild.2012.03.010.
@article{osti_1041424,
title = {Predicting Future Hourly Residential Electrical Consumption: A Machine Learning Case Study},
author = {Edwards, Richard E and New, Joshua Ryan and Parker, Lynne Edwards},
abstractNote = {Whole building input models for energy simulation programs are frequently created in order to evaluate specific energy savings potentials. They are also often utilized to maximize cost-effective retrofits for existing buildings as well as to estimate the impact of policy changes toward meeting energy savings goals. Traditional energy modeling suffers from several factors, including the large number of inputs required to characterize the building, the specificity required to accurately model building materials and components, simplifying assumptions made by underlying simulation algorithms, and the gap between the as-designed and as-built building. Prior works have attempted to mitigate these concerns by using sensor-based machine learning approaches to model energy consumption. However, a majority of these prior works focus only on commercial buildings. The works that focus on modeling residential buildings primarily predict monthly electrical consumption, while commercial models predict hourly consumption. This means there is not a clear indicator of which techniques best model residential consumption, since these methods are only evaluated using low-resolution data. We address this issue by testing seven different machine learning algorithms on a unique residential data set, which contains 140 different sensors measurements, collected every 15 minutes. In addition, we validate each learner's correctness on the ASHRAE Great Energy Prediction Shootout, using the original competition metrics. Our validation results confirm existing conclusions that Neural Network-based methods perform best on commercial buildings. However, the results from testing our residential data set show that Feed Forward Neural Networks, Support Vector Regression (SVR), and Linear Regression methods perform poorly, and that Hierarchical Mixture of Experts (HME) with Least Squares Support Vector Machines (LS-SVM) performs best - a technique not previously applied to this domain.},
doi = {10.1016/j.enbuild.2012.03.010},
journal = {Energy and Buildings},
issn = {0378-7788},
number = 0,
volume = 49,
place = {United States},
year = {2012},
month = {1}
}