Smart meter data-driven voltage forecasting model for a real distribution network based on SCO-MLP

Dokur, E, Sengor, I, Yuzgec, U, Erdogan, N ORCID: https://orcid.org/0000-0003-1621-2748 and Hayes, BP, 2023. Smart meter data-driven voltage forecasting model for a real distribution network based on SCO-MLP. In: Proceedings of 2023 IEEE PES Innovative Smart Grid Technologies EUROPE (ISGT-EUROPE) 23-26 October, 2023. IEEE. ISBN 9798350396799

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Abstract

Advanced metering infrastructure like smart meter technology has enabled the collection of high-resolution data on voltage, active, and reactive power consumption from end-users in real-time. This paper introduces a new machine learning model, named Single Candidate Optimizer (SCO) - Multi-layer perceptron (MLP), for accurate node voltage forecasting in low voltage (LV) distribution networks with high penetrations of low-carbon technologies. The proposed model utilizes historical active and reactive power measurements in one-minute resolution from smart meters to predict node voltage time series values without requiring the network’s electrical model topology and parameters. The computational performance of the MLP framework is improved with the SCO algorithm, which reduces the number of required iterations while maintaining accuracy. The model’s performance is evaluated with numerical metrics and compared against Particle Swarm optimization (PSO) and Differential Evolution (DE)-based models, revealing that the proposed model outperforms both, exhibiting a promising voltage forecasting capability with an average deviation of 1.296 volts relative to the measured values. Overall, this study demonstrates the potential of machine learning and smart meter data for enhancing the stability and efficiency of LV distribution networks.

Item Type:	Chapter in book
Description:	Paper presented at IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE), Grenoble, France, 23-26 October 2023.
Creators:	Dokur, E., Sengor, I., Yuzgec, U., Erdogan, N. and Hayes, B.P.
Publisher:	IEEE
Date:	23 October 2023
ISBN:	9798350396799
Identifiers:	Number Type 10.1109/isgteurope56780.2023.10408345 DOI 1865281 Other
Rights:	© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Divisions:	Schools > School of Science and Technology
Record created by:	Laura Ward
Date Added:	23 Feb 2024 11:22
Last Modified:	23 Feb 2024 11:22
URI:	https://irep.ntu.ac.uk/id/eprint/50925

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