Simultaneous disaggregation of multiple appliances based on non-intrusive load monitoring

Abstract Non-intrusive load monitoring (NILM) can achieve the disaggregation of power consumption through electricity data at the power entrance without changing the existing circuit structure, which is helpful for saving energy, reducing emission, and improving the utilization of electric energy. This paper studies event-based NILM, which can disaggregate multiple appliances that are switched simultaneously. That is a mixed linear integer programming (MILP) model, where the 0–1 indicates whether the appliances were switched in the event process. And the 0–1 variable constraints of the power feature vectors during the event are constructed. The proposed MILP runs only when events occur, which can reduce the computational complexity. In event detection, non-dominated sorting genetic algorithm II is used to select the parameters of cumulative sum (CUSUM), such as threshold and the length of sliding windows, which realizes the tradeoff between precision and recall. The case studies demonstrate that the improved CUSUM has a precision of 92% in event detection; the proposed event-based NILM approach on the REDD database and laboratory data achieves an accuracy of over 90% on power draws; the appliance with long switching process, such as the inverter air conditioner, can also be monitored by the sequential combination.

[1]  Marcos J. Rider,et al.  Nonintrusive Load Monitoring Algorithm Using Mixed-Integer Linear Programming , 2018, IEEE Transactions on Consumer Electronics.

[2]  J. Zico Kolter,et al.  REDD : A Public Data Set for Energy Disaggregation Research , 2011 .

[3]  Jian Liang,et al.  Load Signature Study—Part I: Basic Concept, Structure, and Methodology , 2010, IEEE Transactions on Power Delivery.

[4]  Scott Dick,et al.  Toward Non-Intrusive Load Monitoring via Multi-Label Classification , 2017, IEEE Transactions on Smart Grid.

[5]  Yu-Hsiu Lin,et al.  Modern development of an Adaptive Non-Intrusive Appliance Load Monitoring system in electricity energy conservation , 2012 .

[6]  Xiaowei Luo,et al.  A Supervised Event-Based Non-Intrusive Load Monitoring for Non-Linear Appliances , 2018 .

[7]  Xu Zhang,et al.  Non-intrusive load disaggregation based on deep dilated residual network , 2019, Electric Power Systems Research.

[8]  Shuai Han,et al.  An enhanced ISODATA algorithm for recognizing multiple electric appliances from the aggregated power consumption dataset , 2017 .

[9]  Yongheng Pang,et al.  An Event-Driven Convolutional Neural Architecture for Non-Intrusive Load Monitoring of Residential Appliance , 2020, IEEE Transactions on Consumer Electronics.

[10]  Jian Liang,et al.  Load Signature Study—Part II: Disaggregation Framework, Simulation, and Applications , 2010, IEEE Transactions on Power Delivery.

[11]  Dimitris P. Labridis,et al.  Development of distinct load signatures for higher efficiency of NILM algorithms , 2014 .

[12]  Hao Chen,et al.  Multi-objective evolutionary algorithms applied to non-intrusive load monitoring , 2019 .

[13]  A. Longjun Wang,et al.  Non-intrusive load monitoring algorithm based on features of V–I trajectory , 2018 .

[14]  Chris Develder,et al.  Detection of unidentified appliances in non-intrusive load monitoring using siamese neural networks , 2019, International Journal of Electrical Power & Energy Systems.

[15]  T. Suzuki,et al.  Nonintrusive appliance load monitoring based on integer programming , 2008, 2008 SICE Annual Conference.

[16]  Rene de Jesus Romero-Troncoso,et al.  Identification of the electrical load by C-means from non-intrusive monitoring of electrical signals in non-residential buildings , 2019 .

[17]  G. W. Hart,et al.  Nonintrusive appliance load monitoring , 1992, Proc. IEEE.

[18]  C. Bennett,et al.  Networking AMI Smart Meters , 2008, 2008 IEEE Energy 2030 Conference.

[19]  Tommi S. Jaakkola,et al.  Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation , 2012, AISTATS.

[20]  Zuyi Li,et al.  A Hybrid Event Detection Approach for Non-Intrusive Load Monitoring , 2019, IEEE Transactions on Smart Grid.

[21]  Ivan V. Bajic,et al.  Load Disaggregation Based on Aided Linear Integer Programming , 2016, IEEE Transactions on Circuits and Systems II: Express Briefs.

[22]  Vladimir Stankovic,et al.  On a Training-Less Solution for Non-Intrusive Appliance Load Monitoring Using Graph Signal Processing , 2016, IEEE Access.

[23]  Hamed Ahmadi,et al.  Load Decomposition at Smart Meters Level Using Eigenloads Approach , 2015, IEEE Transactions on Power Systems.

[24]  Jyoti Maggu,et al.  Simultaneous Detection of Multiple Appliances From Smart-Meter Measurements via Multi-Label Consistent Deep Dictionary Learning and Deep Transform Learning , 2019, IEEE Transactions on Smart Grid.

[25]  Youda Liu,et al.  Admittance-based load signature construction for non-intrusive appliance load monitoring , 2018 .

[26]  Wenpeng Luan,et al.  Dynamic time warping based non-intrusive load transient identification , 2017 .

[27]  Minxiang Ye,et al.  Non-intrusive load disaggregation solutions for very low-rate smart meter data , 2020, Applied Energy.

[28]  Dongdong Li,et al.  A nonintrusive load identification method for residential applications based on quadratic programming , 2016 .

[29]  Chi Zhou,et al.  SARAA: Semi-Supervised Learning for Automated Residential Appliance Annotation , 2017, IEEE Transactions on Smart Grid.

[30]  Peng Hin Lee,et al.  Non-intrusive load disaggregation with adaptive estimations of devices main power effects and two-way interactions , 2016 .

[31]  Gregory D. Abowd,et al.  At the Flick of a Switch: Detecting and Classifying Unique Electrical Events on the Residential Power Line (Nominated for the Best Paper Award) , 2007, UbiComp.