Power Transformer Summer Peak Load Prediction Using SCADA and Supervised Learning

Neeraj Kanwar, Divay Bargoti, Vinay Kumar Jadoun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Load forecasting is used to meet the demand and supply equilibrium in any distribution system. It is basically multi-variable and multi-dimension estimation problem. It helps electric utilities to make advance decisions about purchase and generation of electric power, switching of loads and infrastructure expansion, etc. This paper presents peak load prediction of a power transformer from the given datasets. Peak load data are obtained using supervisory control and data acquisition (SCADA). Curve fitting prediction is devised to calculate hourly load forecasting of the week days. However, this method does not provide accurate results as compared to machine learning (ML) algorithms. ML algorithms not only able to review large volumes of data but also provide accurate results with percentage error of only up to 10%. The load data are acquired from the DF 8000 software. To obtain the predictions, multiple regression model is applied to the dataset. A python data science platform is used for simulation work. The results obtained are presented.

Original languageEnglish
Title of host publicationAdvances in Energy Technology - Select Proceedings of EMSME 2020
EditorsRamesh C. Bansal, Anshul Agarwal, Vinay Kumar Jadoun
PublisherSpringer Science and Business Media Deutschland GmbH
Pages215-221
Number of pages7
ISBN (Print)9789811614750
DOIs
Publication statusPublished - 2022
EventInternational Conference on Energy, Material Sciences and Mechanical Engineering, EMSME 2020 - New Delhi, India
Duration: 30-10-202001-11-2020

Publication series

NameLecture Notes in Electrical Engineering
Volume766
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

ConferenceInternational Conference on Energy, Material Sciences and Mechanical Engineering, EMSME 2020
Country/TerritoryIndia
CityNew Delhi
Period30-10-2001-11-20

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering

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