short-paper

Semantic search in household energy consumption segmentation through descriptive characterization

Authors:

BuildSys '19: Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

Pages 263 - 266

https://doi.org/10.1145/3360322.3360865

Published: 13 November 2019 Publication History

Get Access

Abstract

With the widespread adoption of smart metering infrastructures, household energy consumption segmentation is receiving increasing attention. The objective is to transform the large volume of household daily load shapes into representative patterns through clustering methods, with the aim of program targeting and customer engagement. In the literature, there exists a high variation in the number of clusters that different studies have adopted. In order to address the challenge in the trade-off between cluster accuracy and ease of interpretation, in this paper, we introduce a data-driven characterization scheme for resultant clustered load shapes, with the aim of facilitating information retrieval of load shapes with specific semantic attributes. The characterization scheme extracts descriptive features from load shapes to explain their temporal pattern. Using segmentation results on a sample data set from Pecan Street Dataport, we show the feasibility of obtaining the semantic representation of load shapes and performing query analysis by accounting for their similarities. Furthermore, as an application case study, we demonstrated the identification/retrieval of suitable households with specific load types for the adoption of PV-battery system, with average self-sufficiency of 80%.

References

[1]

M. Afzalan and F. Jazizadeh, "Residential loads flexibility potential for demand response using energy consumption patterns and user segments," Applied Energy, vol. 254, p. 113693, 2019.

Crossref

Google Scholar

[2]

J. Kwac, J. Flora, and R. Rajagopal, "Household energy consumption segmentation using hourly data," IEEE Transactions on Smart Grid, vol. 5, no. 1, pp. 420--430, 2014.

Crossref

Google Scholar

[3]

S. Xu, E. Barbour, and M. C. González, "Household segmentation by load shape and daily consumption," in Proc. ACM SigKDD 2017 Conf. Halifax, Nov. Scotia, Canada, August 2017, 2017.

Google Scholar

[4]

E. Barbour and M. González, "Enhancing household-level load forecasts using daily load profile clustering," in Proceedings of the 5th Conference on Systems for Built Environments, 2018, pp. 107--115: ACM.

Google Scholar

[5]

T. Teeraratkul, D. O'Neill, and S. Lall, "Shape-based approach to household electric load curve clustering and prediction," IEEE Transactions on Smart Grid, vol. 9, no. 5, pp. 5196--5206, 2018.

Crossref

Google Scholar

[6]

F. McLoughlin, A. Duffy, and M. Conlon, "A clustering approach to domestic electricity load profile characterisation using smart metering data," Applied energy, vol. 141, pp. 190--199, 2015.

Crossref

Google Scholar

[7]

S. Iyengar, S. Lee, D. Irwin, and P. Shenoy, "Analyzing energy usage on a city-scale using utility smart meters," in Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments, 2016, pp. 51--60: ACM.

Google Scholar

[8]

J. Y. Park, X. Yang, C. Miller, P. Arjunan, and Z. Nagy, "Apples or oranges? Identification of fundamental load shape profiles for benchmarking buildings using a large and diverse dataset," Applied Energy, vol. 236, pp. 1280--1295, 2019.

Crossref

Google Scholar

[9]

Source: Pecan Street Inc. Dataport, 2017.

Google Scholar

[10]

N. P. Watts. Available: https://pvwatts.nrel.gov/pvwatts.php

Google Scholar

[11]

R. Luthander, J. Widén, D. Nilsson, and J. Palm, "Photovoltaic self-consumption in buildings: A review," Applied energy, vol. 142, pp. 80--94, 2015.

Crossref

Google Scholar

Cited By

View all

Becerik-Gerber BLucas GAryal AAwada MBergés MBillington SBoric-Lubecke OGhahramani AHeydarian AHöelscher CJazizadeh FKhan ALangevin JLiu RMarks FMauriello MMurnane ENoh HPritoni MRoll SSchaumann DSeyedrezaei MTaylor JZhao JZhu R(2022)The field of human building interaction for convergent research and innovation for intelligent built environmentsScientific Reports10.1038/s41598-022-25047-y12:1Online publication date: 21-Dec-2022
https://doi.org/10.1038/s41598-022-25047-y
Arnold WSrivastava TSpangher LAgwan USpanos C(2021)Adapting Surprise Minimizing Reinforcement Learning Techniques for Transactive ControlProceedings of the Twelfth ACM International Conference on Future Energy Systems10.1145/3447555.3466590(488-492)Online publication date: 22-Jun-2021
https://dl.acm.org/doi/10.1145/3447555.3466590
Afzalan MJazizadeh FEldardiry H(2021)Two-Stage Clustering of Household Electricity Load Shapes for Improved Temporal Pattern RepresentationIEEE Access10.1109/ACCESS.2021.31220829(151667-151680)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3122082
Show More Cited By

Index Terms

Semantic search in household energy consumption segmentation through descriptive characterization

Recommendations

Histogram-based automatic segmentation of images

The segmentation process is defined by separating the objects as clustering in the images. The most used method in the segmentation is k-means clustering algorithm. k-means clustering algorithm needs the number of clusters, the initial central points of ...
MRI segmentation fusion for brain tumor detection

A new clustering algorithm, Potential Field Segmentation (PFS), is proposed.The algorithm is used for the automatic segmentation of brain tumors in MRI images.Ensemble methods that fuse PFS and other segmentation algorithms are proposed.Proposed ...
RPCCL Clustering and Its Evaluation in Image Segmentation
ICCSEE '12: Proceedings of the 2012 International Conference on Computer Science and Electronics Engineering - Volume 03

In this paper, we propose a new image segmentation approach based on rival penalized controlled competitive learning (RPCCL) and color quantization technique. First, we perform median filtering on input image. Second, the initial color centers are ...

Comments

Information & Contributors

Information

Published In

BuildSys '19: Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

November 2019

413 pages

ISBN:9781450370059

DOI:10.1145/3360322

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 November 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper
Research
Refereed limited

Conference

BuildSys '19

Sponsor:

SIGEnergy

BuildSys '19: The 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

November 13 - 14, 2019

NY, New York, USA

Acceptance Rates

BuildSys '19 Paper Acceptance Rate 40 of 131 submissions, 31%;

Overall Acceptance Rate 148 of 500 submissions, 30%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
187
Total Downloads

Downloads (Last 12 months)8
Downloads (Last 6 weeks)5

Reflects downloads up to 07 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Becerik-Gerber BLucas GAryal AAwada MBergés MBillington SBoric-Lubecke OGhahramani AHeydarian AHöelscher CJazizadeh FKhan ALangevin JLiu RMarks FMauriello MMurnane ENoh HPritoni MRoll SSchaumann DSeyedrezaei MTaylor JZhao JZhu R(2022)The field of human building interaction for convergent research and innovation for intelligent built environmentsScientific Reports10.1038/s41598-022-25047-y12:1Online publication date: 21-Dec-2022
https://doi.org/10.1038/s41598-022-25047-y
Arnold WSrivastava TSpangher LAgwan USpanos C(2021)Adapting Surprise Minimizing Reinforcement Learning Techniques for Transactive ControlProceedings of the Twelfth ACM International Conference on Future Energy Systems10.1145/3447555.3466590(488-492)Online publication date: 22-Jun-2021
https://dl.acm.org/doi/10.1145/3447555.3466590
Afzalan MJazizadeh FEldardiry H(2021)Two-Stage Clustering of Household Electricity Load Shapes for Improved Temporal Pattern RepresentationIEEE Access10.1109/ACCESS.2021.31220829(151667-151680)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3122082
Y KM WR A(2021)Predicting Winners and Losers under Time-Of-Use Tariffs Using Smart Meter DataEnergy10.1016/j.energy.2021.121438(121438)Online publication date: Jul-2021
https://doi.org/10.1016/j.energy.2021.121438
Afzalan MJazizadeh F(2020)A Machine Learning Framework to Infer Time-of-Use of Flexible Loads: Resident Behavior Learning for Demand ResponseIEEE Access10.1109/ACCESS.2020.30021558(111718-111730)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3002155

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Histogram-based automatic segmentation of images

MRI segmentation fusion for brain tumor detection

RPCCL Clustering and Its Evaluation in Image Segmentation

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations