Proficiency of Power Values for Load Disaggregation

Load disaggregation techniques infer the operation of different power-consuming devices from a single measurement point that records the total power drawn over time. Thus, a device consuming power at the moment can be understood as information encoded in the power draw. However, similar power draws or similar combinations of power draws limit the ability to detect the currently active device set. We present an information coding perspective of load disaggregation to enable a better understanding of this process and support its future improvement. In typical cases of quantity and types of devices and their respective power consumption, not all possible device configurations can be mapped to distinguishable power values. We introduce the term proficiency to describe the suitability of a device set for load disaggregation. We provide the notion and calculation of entropy of the initial device states, mutual information of power values, and the resulting uncertainty coefficient or proficiency. We show that the proficiency is highly dependent on the device running probability, especially for devices with multiple states of power consumption. The application of the concept is demonstrated by artificial data as well as with actual power consumption data from real-world power draw data sets.

[1]  M. Baranski,et al.  Genetic algorithm for pattern detection in NIALM systems , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[2]  J. V. White,et al.  Performance Metrics for Group-Detection Algorithms , 2004 .

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

[4]  J. Merckle,et al.  Smart meter systems detection & classification using artificial neural networks , 2012, IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society.

[5]  Yu-Hsiu Lin,et al.  Non-Intrusive Load Monitoring by Novel Neuro-Fuzzy Classification Considering Uncertainties , 2014, IEEE Transactions on Smart Grid.

[6]  Matthew J. Johnson,et al.  Bayesian nonparametric hidden semi-Markov models , 2012, J. Mach. Learn. Res..

[7]  Muhammad Ali Imran,et al.  Low-power appliance monitoring using Factorial Hidden Markov Models , 2013, 2013 IEEE Eighth International Conference on Intelligent Sensors, Sensor Networks and Information Processing.

[8]  Manish Marwah,et al.  A Temporal Motif Mining Approach to Unsupervised Energy Disaggregation: Applications to Residential and Commercial Buildings , 2013, AAAI.

[9]  Dominik Egarter,et al.  PALDi: Online Load Disaggregation via Particle Filtering , 2015, IEEE Transactions on Instrumentation and Measurement.

[10]  Silvia Santini,et al.  The ECO data set and the performance of non-intrusive load monitoring algorithms , 2014, BuildSys@SenSys.

[11]  Jing Liao,et al.  A low-complexity energy disaggregation method: Performance and robustness , 2014, 2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG).

[12]  A.C. Liew,et al.  Neural-network-based signature recognition for harmonic source identification , 2006, IEEE Transactions on Power Delivery.

[13]  Thomas M. Cover,et al.  Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing) , 2006 .

[14]  Manish Marwah,et al.  Unsupervised Disaggregation of Low Frequency Power Measurements , 2011, SDM.

[15]  Muhammad Ali Imran,et al.  Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey , 2012, Sensors.

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

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

[18]  Dominik Egarter,et al.  Complexity of Power Draws for Load Disaggregation , 2015, ArXiv.

[19]  Sundeep Pattem.  Unsupervised Disaggregation for Non-intrusive Load Monitoring , 2012, 2012 11th International Conference on Machine Learning and Applications.

[20]  Oliver Kramer,et al.  On Ensemble Classifiers for Nonintrusive Appliance Load Monitoring , 2012, HAIS.

[21]  Lorenzo Peretto,et al.  The role of measurements in the smart grid era , 2010, IEEE Instrumentation & Measurement Magazine.

[22]  Alex Rogers,et al.  An unsupervised training method for non-intrusive appliance load monitoring , 2014, Artif. Intell..

[23]  Jing Liao,et al.  Non-intrusive appliance load monitoring using low-resolution smart meter data , 2014, 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[24]  Jovica V. Milanovic,et al.  Artificial-Intelligence-Based Methodology for Load Disaggregation at Bulk Supply Point , 2015, IEEE Transactions on Power Systems.

[25]  Mario Berges,et al.  Unsupervised disaggregation of appliances using aggregated consumption data , 2011 .

[26]  Dietmar Bruckner,et al.  A hidden Markov model based procedure for identifying household electric loads , 2011, IECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics Society.

[27]  Thomas M. Cover,et al.  Elements of Information Theory: Cover/Elements of Information Theory, Second Edition , 2005 .

[28]  Steven B. Leeb,et al.  Nonintrusive Load Monitoring and Diagnostics in Power Systems , 2008, IEEE Transactions on Instrumentation and Measurement.

[29]  Andrea Monacchi,et al.  GREEND: An energy consumption dataset of households in Italy and Austria , 2014, 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm).

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

[31]  Henrik Ohlsson,et al.  Fundamental limits of nonintrusive load monitoring , 2013, HiCoNS.

[32]  Bernardete Ribeiro,et al.  Electrical Signal Source Separation Via Nonnegative Tensor Factorization Using On Site Measurements in a Smart Home , 2014, IEEE Transactions on Instrumentation and Measurement.

[33]  Michael Zeifman,et al.  Nonintrusive appliance load monitoring: Review and outlook , 2011, IEEE Transactions on Consumer Electronics.