Paper Info
Title
On Electrical Load Disaggregation using Recurrent Neural Networks.
Abstract
In a residential setting, Load disaggregation (LD) is about obtaining appliance-specific operational details in terms of time and power consumption by processing aggregate power consumption data. The disaggregated load information helps utilities to categorize customers based on their usage patterns, facilitating optimal demand response design. Further, LD helps customers to know about their energy-consuming behavior, which is beneficial in reducing the consumption. To be able to provide appliance-specific consumption patterns for aforementioned goals, apart from accurate load identification, estimates of energy consumption of appliances of interest are necessary. In short, it is essential to cull out operational waveform of each of the requisite appliance. Towards this end, very few results have been reported in the literature related to estimating the operational wave-forms, even for large power consuming appliances. In this work, we address this problem using a deep-learning architecture with Recurrent Neural Networks (RNN) variants like Long-Short Term Memory networks (LSTM) and Generalized Recurrent Unit networks (GRU). In addition, a simple but effective technique in pre-processing of the aggregated data is proposed and implemented to identify and reconstruct the consumption pattern of low-power consuming appliances like Refrigerator.
Year
DOI
Venue
2019
10.1145/3360322.3361002
BuildSys '19: The 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation New York NY USA November, 2019
Keywords
Field
DocType
Load Disaggregation,Recurrent Neural Networks,LSTM,GRU
Electrical load,Computer science,Recurrent neural network,Control engineering
Conference
ISBN
Citations 
PageRank 
978-1-4503-7005-9
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Rajasekhar Gopu100.34
Anusha Gudimallam200.34
Vishnu B300.34
Naveen Kumar Thokala401.35
M. Girish Chandra511224.49