Paper Info
Title
Low Sampling Rate Electrical Load Disaggregation Using Dictionary Representation and Graph Signal Smoothness.
Abstract
The present power sector is witnessing demand-side deregulations across the world, coupled with the ongoing deployment of smart meters providing the data at 15 or 30 minutes intervals. There is a need to analyze this data to provide valuable inputs and insights for different players in the ecosystem like, consumers, aggregators, retailers and utilities. But, disaggregating the low granular data is highly challenging since they lack events and signatures of constituent loads. In this paper, an effort has been made to combine the concepts of dictionary learning and graph signal processing to arrive at a novel methodology to disaggregate major power consuming loads in residential buildings. Dictionaries are used to characterize the different loads in terms of power values and time of operation. The coefficients thus obtained are treated as graph signals and graph smoothness is used to propagate the coefficients from the known training phase to the test phase. The paper presents the optimization formulation, the derivation of the requisite solution steps to identify the loads of interest and estimate their power consumption. Towards demonstrating the usefulness of the technique, typical results for both simulated and real data are provided.
Year
DOI
Venue
2019
10.1145/3307772.3328292
E-ENERGY'19: PROCEEDINGS OF THE 10TH ACM INTERNATIONAL CONFERENCE ON FUTURE ENERGY SYSTEMS
Keywords
Field
DocType
Dictionary learning,Graph smoothness,Load disaggregation
Graph,Software deployment,Dictionary learning,Metre,Electrical load,Computer science,Sampling (signal processing),Smoothness,Computer engineering,Power consumption
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Kriti Kumar135.15
M. Girish Chandra211224.49
Achanna Anil Kumar3245.39
Naveen Kumar Thokala401.35