Paper Info
Title
Solar PV Detection Using an Optimal Template Approach with Genetic Algorithm
Abstract
With the increasing popularity of domestic solar PV systems there is a need for smart grid network operators to be able to identify solar PV systems attached to their networks. This need is driven by human safety, equipment safety, and regulatory compliance concerns. Given the implementation of smart metering as part of the evolution toward smart grids and the availability of smart metering data, methods that automate the identification of solar PV systems from consumption data are needed to address these concerns. This paper proposes an optimal template approach with genetic algorithm for solar PV detection, which successfully classifies solar PV and non-solar PV customers by utilising genetic algorithm optimisation to find optimal template pairs and matching observations to the closest template. This is done by using domain knowledge to specify a template parameterisation specific to the problem and using genetic algorithm optimisation to find template pairs that are optimised for accuracy.
Year
DOI
Venue
2021
10.1109/IECON48115.2021.9589481
IECON 2021 - 47TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY
Keywords
DocType
ISSN
Smart metering data, optimal template approach, solar PV detection, genetic algorithm, classification
Conference
1553-572X
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Wenhua Ling100.68
Geordie Dalzell200.68
Xinghuo Yu332.74
Brendan P. McGrath400.34
Peter Sokolowski501.35