Forecasting Method of LV Distribution’s Load CurveBy Means of Machine Learning Utilizing Smart Meter Data

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CIRED 2019 - 806.pdf (1.27 MB)

Paper number

806

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Conference name

CIRED 2019

Conference date

3-6 June 2019

Conference location

Madrid, Spain

Peer-reviewed

Yes

Short title

Convener

Authors

Kanazawa, Yuki, Chubu Electric Power Company, Japan
Ishikawa, Hiroyuki, Chubu Electric Power Company, Japan
Uenishi, Hirokazu, Chubu Electric Power Company, Japan
Ichinomiya, Hiroki, Mitsubishi Research Institute, Japan

Abstract

Maximum current is necessary to be estimated when designing a LV distribution grid, i.e. MV/LV pole-mounted transformers and LV feeders, which supplies customers of several in minimum and dozens in maximum. This is currently estimated from the total contract capacities of the customers supplied by the grid, multiplying diversity and demand factor corresponding to the number of customers. This method is rather efficient and simple because the calculation can be done simply by using the contract capacities and the numbers of contracts.However, there are some constraints in this method. Diversity and demand factors may not be correct as they were derived statistically from sampled current measurements of customers. Additionally, this research was carried out years ago and have never been updated as it takes vast time and effort to re-measure them. As a result, these factors may be out-of-date as recent changes in electricity usage are not taken into consideration, such as the introduction of highly-efficient appliances or the diversities in lifestyles of customers. Thus, there is a possibility that the current estimated from the conventional method which is used for designing the grid diverge much from the actual state. This leads to over investment.This paper describes our new methodology for estimating the load curves of newly-connected customers based on the off-line information, such as contract capacity, contract types, location of the customer, presence of PV, etc.

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Publisher

AIM

Date

2019-06-03

Published in

CIRED 2019 Conference

Permanent link to this record

https://cired-repository.org/handle/20.500.12455/155
http://dx.doi.org/10.34890/310

ISSN

2032-9644

ISBN

978-2-9602415-0-1