skip to main content
10.1145/2821650.2821664acmconferencesArticle/Chapter ViewAbstractPublication PagesbuildsysConference Proceedingsconference-collections
research-article

Sometimes, Money Does Grow On Trees: Data-Driven Demand Response with DR-Advisor

Published: 04 November 2015 Publication History

Abstract

Real-time electricity pricing and demand response has become a clean, reliable and cost-effective way of mitigating peak demand on the electricity grid. We consider the problem of end-user demand response (DR) for large commercial buildings which involves predicting the demand response baseline, evaluating fixed DR strategies and synthesizing DR control actions for load curtailment in return for a financial reward. Using historical data from the building, we build a family of regression trees and learn data-driven models for predicting the power consumption of the building in real-time. We present a method called DR-Advisor called DR-Advisor, which acts as a recommender system for the building's facilities manager and provides suitable control actions to meet the desired load curtailment while maintaining operations and maximizing the economic reward. We evaluate the performance of DR-Advisor for demand response using data from a real office building and a virtual test-bed.

References

[1]
Energy price risk and the sustainability of demand side supply chains. Applied Energy, 123(0):327--334, 2014.
[2]
D. Auslander, D. Caramagno, D. Culler, T. Jones, A. Krioukov, M. Sankur, J. Taneja, J. Trager, S. Kiliccote, R. Yin, et al. Deep demand response: The case study of the citris building at the university of california-berkeley.
[3]
L. Breiman. Random forests. Machine learning, 45(1):5--32, 2001.
[4]
L. Breiman, J. Friedman, C. J. Stone, and R. A. Olshen. Classification and regression trees. CRC press, 1984.
[5]
F. E. R. Commission et al. Assessment of demand response and advanced metering. 2012.
[6]
Con Edison. Demand response programs details.
[7]
D. B. Crawley, L. K. Lawrie, et al. Energyplus: creating a new-generation building energy simulation program. Energy and Buildings, 33(4):319--331, 2001.
[8]
M. Deru, K. Field, D. Studer, et al. U.s. department of energy commercial reference building models of the national building stock. 2010.
[9]
R. E. Edwards, J. New, and L. E. Parker. Predicting future hourly residential electrical consumption: A machine learning case study. Energy and Buildings, 49:591--603, 2012.
[10]
J. Elith, J. R. Leathwick, and T. Hastie. A working guide to boosted regression trees. Journal of Animal Ecology, 77(4):802--813, 2008.
[11]
J. H. Friedman. Multivariate adaptive regression splines. The annals of statistics, pages 1--67, 1991.
[12]
C. Giraud-Carrier. Beyond predictive accuracy: what? Proceedings of the ECML-98 Workshop on Upgrading Learning to Meta-Level: Model Selection and Data Transformation, pages 78--85, 1998.
[13]
C. Goldman. Coordination of energy efficiency and demand response. Lawrence Berkeley National Laboratory, 2010.
[14]
T. Hastie, R. Tibshirani, J. Friedman, T. Hastie, J. Friedman, and R. Tibshirani. The elements of statistical learning, volume 2. Springer, 2009.
[15]
T. Hong, W.-K. Chang, and H.-W. Lin. A fresh look at weather impact on peak electricity demand and energy use of buildings using 30-year actual weather data. Applied Energy, 111:333--350, 2013.
[16]
P. Interconnection. 2014 deamand response operations markets activity report. 2014.
[17]
J. M. Melillo, T. Richmond, and G. W. Yohe. Climate change impacts in the united states: the third national climate assessment. US Global change research program, 841, 2014.
[18]
J. R. New, J. Sanyal, M. Bhandari, and S. Shrestha. Autotune e building energy models. Proceedings of the 5th National SimBuild of IBPSA-USA, 2012
[19]
New-England ISO. Real-time maps and charts, 2013.
[20]
F. Oldewurtel, D. Sturzenegger, G. Andersson, M. Morari, and R. S. Smith. Towards a standardized building assessment for demand response. In Decision and Control (CDC), IEEE 52nd Annual Conference on. IEEE, 2013.
[21]
J. R. Quinlan et al. Learning with continuous classes. In 5th Australian joint conference on artificial intelligence, volume 92, pages 343--348. Singapore, 1992.
[22]
N. Research. Demand response for commercial & industrial markets market players and dynamics, key technologies, competitive overview, and global market forecasts. 2015.
[23]
D. Sturzenegger, D. Gyalistras, M. Morari, and R. Smith. Model predictive climate control of a swiss office building: Implementation, results, and cost-benefit analysis. Control Systems Technology, IEEE Transactions on, 2015.
[24]
M. Taleghani, M. Tenpierik, S. Kurvers, and A. van den Dobbelsteen. A review into thermal comfort in buildings. Renewable and Sustainable Energy Reviews, 26:201--215, 2013.
[25]
A. Vaghefi, M. Jafari, E. Bisse, Y. Lu, and J. Brouwer. Modeling and forecasting of cooling and electricity load demand. Applied Energy, 136:186--196, 2014.
[26]
A. Vellido, M. Guerrero, and P. Lisboa. Making machine learning models interpretable. In In Proc. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2012.
[27]
P. Xu, P. Haves, M. A. Piette, and J. Braun. Peak demand reduction from pre-cooling with zone temperature reset in an office building. Berkeley National Laboratory, 2004.
[28]
L. Yang, H. Yan, and J. C. Lam. Thermal comfort and building energy consumption implications--a review. Applied Energy, 115:164--173, 2014.
[29]
W. Yin, Y. Simmhan, and V. K. Prasanna. Scalable regression tree learning on hadoop using openplanet. In Proceedings of third international workshop on MapReduce and its Applications Date, pages 57--64. ACM, 2012.
[30]
E.Záčeková, Z. Váa, ñand J. Cigler. Towards the real-life implementation of mpc for an office building: Identification issues. Applied Energy, 135:53--62, 2014.

Cited By

View all
  • (2023)A Systematic Review on Demand Response Role Toward Sustainable Energy in the Smart Grids-Adopted Buildings SectorIEEE Access10.1109/ACCESS.2023.328764111(64968-65027)Online publication date: 2023
  • (2022)Survey data on university students’ experience of energy control, indoor comfort, and energy flexibility in campus buildingsEnergy Informatics10.1186/s42162-022-00239-y5:S4Online publication date: 21-Dec-2022
  • (2019)One for all, All for oneProceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3360322.3360850(277-286)Online publication date: 13-Nov-2019
  • Show More Cited By

Index Terms

  1. Sometimes, Money Does Grow On Trees: Data-Driven Demand Response with DR-Advisor

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Conferences
      BuildSys '15: Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments
      November 2015
      264 pages
      ISBN:9781450339810
      DOI:10.1145/2821650
      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]

      Sponsors

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 04 November 2015

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. cyber physical systems
      2. demand response
      3. machine learning

      Qualifiers

      • Research-article

      Funding Sources

      • NSF MRI
      • STARnet Terraswarm

      Conference

      Acceptance Rates

      BuildSys '15 Paper Acceptance Rate 20 of 66 submissions, 30%;
      Overall Acceptance Rate 148 of 500 submissions, 30%

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)6
      • Downloads (Last 6 weeks)0
      Reflects downloads up to 03 Mar 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2023)A Systematic Review on Demand Response Role Toward Sustainable Energy in the Smart Grids-Adopted Buildings SectorIEEE Access10.1109/ACCESS.2023.328764111(64968-65027)Online publication date: 2023
      • (2022)Survey data on university students’ experience of energy control, indoor comfort, and energy flexibility in campus buildingsEnergy Informatics10.1186/s42162-022-00239-y5:S4Online publication date: 21-Dec-2022
      • (2019)One for all, All for oneProceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3360322.3360850(277-286)Online publication date: 13-Nov-2019
      • (2019)Optimizing Viral Marketing for Demand Response2019 11th International Conference on Communication Systems & Networks (COMSNETS)10.1109/COMSNETS.2019.8711364(714-719)Online publication date: Jan-2019
      • (2017)Incentive design for demand-response based on building constraintsProceedings of the 4th ACM International Conference on Systems for Energy-Efficient Built Environments10.1145/3137133.3137142(1-10)Online publication date: 8-Nov-2017
      • (2017)Design Automation of Cyber-Physical Systems: Challenges, Advances, and OpportunitiesIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2016.263396136:9(1421-1434)Online publication date: 1-Sep-2017
      • (2017)Demand response with model predictive comfort compliance in an office building2017 IEEE International Conference on Smart Grid Communications (SmartGridComm)10.1109/SmartGridComm.2017.8340733(351-356)Online publication date: Oct-2017
      • (2016)Demand response in commercial buildings with an Assessable impact on occupant comfort2016 IEEE International Conference on Smart Grid Communications (SmartGridComm)10.1109/SmartGridComm.2016.7778802(447-452)Online publication date: Nov-2016
      • (2016)Interactive analytics for smart cities infrastructures2016 1st International Workshop on Science of Smart City Operations and Platforms Engineering (SCOPE) in partnership with Global City Teams Challenge (GCTC) (SCOPE - GCTC)10.1109/SCOPE.2016.7515055(1-6)Online publication date: 11-Apr-2016
      • (2016)Three challenges in cyber-physical systems2016 8th International Conference on Communication Systems and Networks (COMSNETS)10.1109/COMSNETS.2016.7440015(1-8)Online publication date: Jan-2016

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Figures

      Tables

      Media

      Share

      Share

      Share this Publication link

      Share on social media