Nonintrusive Load Monitoring Algorithm Using Mixed-Integer Linear Programming

This paper presents a nonintrusive load monitoring (NILM) algorithm based on mixed-integer linear programming. The formulation deals with the problem of multiple switching that arises when disaggregating individual appliance's consumptions from a compound power measurement. Mixed-integer linear...

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Bibliographic Details
Published in:IEEE transactions on consumer electronics Vol. 64; no. 2; pp. 180 - 187
Main Authors: Wittmann, Fernando Marcos, Lopez, Juan Camilo, Rider, Marcos J.
Format: Journal Article
Language:English
Published: New York IEEE 01.05.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Energy disaggregation
Energy management
Hidden Markov models
Home appliances
Integer programming
Linear programming
Load modeling
load signature
mathematical programming
Measuring instruments
Microsoft Windows
Model accuracy
Monitoring
nonintrusive load monitoring (NILM)
Pattern recognition
Power consumption
Power measurement
Reactive power
Residential energy
Signatures
ISSN:0098-3063, 1558-4127
Online Access:Get full text
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Summary:This paper presents a nonintrusive load monitoring (NILM) algorithm based on mixed-integer linear programming. The formulation deals with the problem of multiple switching that arises when disaggregating individual appliance's consumptions from a compound power measurement. Mixed-integer linear constraints are used to efficiently represent the load signatures of each appliance. Also, a window-based strategy is used to enhance the computational performance of the proposed NILM algorithm. The disaggregation can be made using only active power measurements at a low sampling rate, which is available in most energy meters. Moreover, if available, other signatures can be added to the model to improve its accuracy, such as reactive power signatures or harmonics. The performance of the algorithm is evaluated using three test cases from the almanac of minutely power dataset. The proposed method is also compared with a disaggregation method called aided linear integer programming. Results demonstrate the ability of the proposed method to accurately identify and allocate individual energy signatures in a computationally efficient manner, which makes it suitable for inexpensive home energy management.
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ISSN:0098-3063
1558-4127
DOI:10.1109/TCE.2018.2843292