A Two-Stage Mixed Integer Programming Model for Distributionally Robust State-Based Non-Intrusive Load Monitoring

This paper presents a non-intrusive load monitoring (NILM) model based on two-stage mixed-integer linear programming theory. Compared with other mixed integer optimization-based models, this paper model introduces fewer integer variables and richer absolute error function of load decomposition, whic...

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Bibliographic Details
Published in:IEEE transactions on consumer electronics Vol. 71; no. 1; pp. 1024 - 1033
Main Authors: Zhang, Chen, Chai, Zimo, Yang, Linfeng
Format: Journal Article
Language:English
Published: New York IEEE 01.02.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Accuracy
Computational efficiency
Computational modeling
Decomposition
distributionally robust
Error functions
Event detection
Feature extraction
Hidden Markov models
Integer programming
Linear programming
Load modeling
Load monitoring
load power decomposition
Mixed integer
Monitoring
Non-intrusive load monitoring
Optimization
Power consumption
Power management
Robustness
Training
two-stage mixed integer programming
ISSN:0098-3063, 1558-4127
Online Access:Get full text
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Summary:This paper presents a non-intrusive load monitoring (NILM) model based on two-stage mixed-integer linear programming theory. Compared with other mixed integer optimization-based models, this paper model introduces fewer integer variables and richer absolute error function of load decomposition, which makes the power state selection of each device more accurate and the power consumption more accurate fitting. First, to tackle the issues related to noisy load data, an innovative load feature extraction model based on Kullback-Leibler distributionally robust optimization principles is introduced. Then the key features (power boundary/fluctuation features) of each device identified through this robust model are integrated into the constraints of the two-stage NILM model. The two-stage complementary framework includes: the determination of device state interval in the first stage; and the accurate fitting of device power consumption within the device state interval in the second stage. Comparative validation against existing optimization-based models on the AMPds, REFIT, and actual laboratory data sets demonstrate that our proposed model significantly enhances power decomposition accuracy and computational efficiency. In addition, the two-stage complementary framework and load feature extraction model can be applied to other optimization-based models of NILM to improve the computational efficiency of each model and the accuracy of load decomposition.
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ISSN:0098-3063
1558-4127
DOI:10.1109/TCE.2025.3530422