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Signature Redacted___Rol Disaggregation of Residential Home Energy via Non-Intrusive Load Monitoring for Energy Savings and Targeted Demand Response by Jeremy Hare B.S., Aerospace Engineering, University of Michigan - Ann Arbor, 2006 M.S., Aerospace Engineering, University of Michigan - Ann Arbor, 2007 Submitted to the Department of Civil and Environmental Engineering and the MIT Sloan School of Management in partial fulfillment of the requirements for the degrees of Master of Business Administration and Master of Science in Civil and Environmental Engineering in conjunction with the Leaders for Global Operations Program at the Massachusetts Institute of Technology June 2018 C Jeremy Hare. All rights reserved. The author hereby grants to MIT pennission to reproduce and to distribute publicly paper and electronic copies of this thesis document in whole or in part in any medium now known or hereafter created. Signature redacted Signature of Author: S r Department of Civil and Envircfimei al Engineering and MIT Sloan School of Management May 11, 2018 redacted Certified By: _Signature Georgia Perakis, Thesis Advisor liam F. Pounds Professor of Management Science Z70 Certified By: Signature red acted John R. Williams, Thesis Advisor /, ro~e/sor of Civil and Environmental Engineering Certified By: Signature redacted Kostya Turitsyn, Thesis Advisor iate Professor oSMechanical Engineering Approved By: Signature redacted Herson Director, MBA oqram/4JT Sloan School of Management Approved By: ___Signature redacted_________rol Jesse Kroll Chair, Graduate Program Comn/ittee, Civil and Environmental Engineering MASSACHUSETTS INSTITUTE OF TECHNOLOGY co W 18 7 20 L JUN LIt3RARIE.S Disaggregation of Residential Home Energy via Non-Intrusive Load Monitoring for Energy Savings and Targeted Demand Response by Jeremy Hare Submitted to the Department of Civil and Environmental Engineering and the MIT Sloan School of Management on May 11, 2014, in partial fulfillment of the requirements for the degrees of Master of Science in Civil and Environmental Engineering and Master of Business Administration Abstract Residential energy disaggregation is a process by which the power usage of a home is broken down into the consumption of individual appliances. There are a number of different methods to perform energy disaggregation, from simulation models to installing "smart-plugs" at every outlet where an appliance is connected to the wall. Non-Intrusive Load Monitoring (NILM) is one such disaggregation option. NILM is widely recognized as one of the most cost-effective methods for gathering disaggregated energy data while maintaining a high level of accuracy. Although the technology has existed for many years, the adoption rate of NILM, and other devices that disaggregate energy, has been minimal. This thesis provides details on the potential benefits, both for the customer and utility provider, associated with furthering the adoption of NILM devices and obtaining the disaggregated appliance level energy-use. A broad overview of potential benefits is presented; however, the primary goal of this thesis will be to investigate two benefits of NILM in detail: overall household energy reduction and targeted demand response. First, installation of a NILM device can provide electricity customers information that allows them to become more aware of their energy consumption, and thereby, more energy efficient. A study was conducted that looked at the electricity consumption of 174 homes that were using a passive NILM device in their home. This NILM device provided immediate feedback on the power consumption for a portion of the home's appliances via smart-phone application. The homes reduced their monthly energy consumption by an average of 2.6 - 3.1% after the NILM installation. This was validated by a number of analysis methods returning I similar results. Aligned with this benefit comes a recommendation for an incentive structure that can reduce the price paid by the consumer and develop a higher adoption rate of NILM devices. Second, the wide-spread adoption of NILM devices can provide electric utilities information to reduce carbon intensity via targeted demand response. There is a significant opportunity for utilities to engage their customers based on the time of use of detailed appliances. Multiple metrics are presented in this thesis to quantify the deferrable load opportunity of specific appliances and individual households. Utility operational cost savings and greater customer incentives can be linked to the use of these metrics. Thesis Supervisor: Georgia Perakis Title: Professor, Operations Research and Operations Management Thesis Supervisor: John Williams Title: Professor, Civil and Environmental Engineering Thesis Supervisor: Kostya Turitsyn Title: Associate Professor, Department of Mechanical Engineering 2 This Page Intentional Left Blank 32 Acknowledgements To my thesis advisors Georgia Perakis, Kostya Turitsyn, John Williams, thank you for your willingness to work through issues and challenge my ideas. To my Company X supervisors, thank you for the flexibility and support of this research. Additional thanks to my colleagues and advisors at Company X, thank you all for your time and energy - it was great working with each one of you over the six-month research fellowship. To the LGO program - this was one of the most challenging, yet rewarding experiences of my life. Thanks to everyone who have made the classes, internships, partner company relationships, and the MIT community work so well to complement one another. To my LGO classmates - It was a privilege to have gotten to know each one of you over the last two years! It is a great honor to be a part of the LGO Class of 2018! And I want to thank my better half - my wife Maria. You make me a better person every day with your encouragement, patience, and love. 4 This Page Intentional Left Blank 5 Contents Chapter 1 - Introduction ........................................................................................................................ 9 1.1 Problem Statem ent ..................................................................................................................... 9 1.2 NILM Overview .......................................................................................................................... 10 1.3 M ethods of Disaggregation ................................................................................................... 11 1.3.1 Surveys ............................................................................................................................... 11 1.3.2 Direct M etering of Appliances ........................................................................................ 12 1.3.3 Statistical M odeling via AMI .......................................................................................... 13 1.3.4 Hardware Sensors .............................................................................................................. 14 1.4 NILM Literature review ............................................................................................................. 15 1.4.1 NILM for Energy Efficiency ................................................................................................. 17 1.4.2 NILM for Targeted Dem and Response ............................................................................... 18 1.5 Thesis Contributions..................................................................................................................19 Chapter 2 - Residential Energy Consum ption Reduction ................................................................ 20 2.1 Introduction .............................................................................................................................. 20 2.2 Data for Study ........................................................................................................................... 20 2.3 Experim ental Setup ................................................................................................................... 22 2.4 Analysis M ethods and Results............................................................................................... 23 2.4.1 M odel 1 - Basic Average Com parison ........................................................................... 23 2.4.2 M odel 2 - M onthly Norm alization ................................................................................. 25 2.4.3 M odel 3 - Seasonality Rem oved & M onthly Norm alization ........................................... 26 2.4.4 M odel 4 - Seasonality Removed with Linear Regression............................................. 27 2.4.5 M odel 5 - Seasonality Rem oved with Linear Regression+ ........................................... 29 2.4.6 M odel 6 - Consistent Tim e Frame Com parison ............................................................ 31 2.5 Sum m ary of Results...................................................................................................................32 2.6 Lim itations and Recom mendations...................................................................................... 33 2.7 Policy Recom m endation ........................................................................................................... 35 Chapter 3 - Targeted Dem and Response ....................................................................................... 38 3.1 Introduction .............................................................................................................................
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