Power Transformer Summer Peak Load Prediction Using SCADA and Supervised Learning

Neeraj Kanwar; Divay Bargoti; Vinay Kumar Jadoun

doi:10.1007/978-981-16-1476-7_21

Power Transformer Summer Peak Load Prediction Using SCADA and Supervised Learning

Neeraj Kanwar^*
, Divay Bargoti
, Vinay Kumar Jadoun

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Advances in Energy Technology - Select Proceedings of EMSME 2020
Editors	Ramesh C. Bansal, Anshul Agarwal, Vinay Kumar Jadoun
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	215-221
Number of pages	7
ISBN (Print)	9789811614750
DOIs	https://doi.org/10.1007/978-981-16-1476-7_21
Publication status	Published - 2022
Event	International Conference on Energy, Material Sciences and Mechanical Engineering, EMSME 2020 - New Delhi, India Duration: 30-10-2020 → 01-11-2020

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	766
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	International Conference on Energy, Material Sciences and Mechanical Engineering, EMSME 2020
Country/Territory	India
City	New Delhi
Period	30-10-20 → 01-11-20

All Science Journal Classification (ASJC) codes

Industrial and Manufacturing Engineering

Access to Document

10.1007/978-981-16-1476-7_21

Cite this

Kanwar, N., Bargoti, D., & Jadoun, V. K. (2022). Power Transformer Summer Peak Load Prediction Using SCADA and Supervised Learning. In R. C. Bansal, A. Agarwal, & V. K. Jadoun (Eds.), Advances in Energy Technology - Select Proceedings of EMSME 2020 (pp. 215-221). (Lecture Notes in Electrical Engineering; Vol. 766). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-16-1476-7_21

Kanwar, Neeraj ; Bargoti, Divay ; Jadoun, Vinay Kumar. / Power Transformer Summer Peak Load Prediction Using SCADA and Supervised Learning. Advances in Energy Technology - Select Proceedings of EMSME 2020. editor / Ramesh C. Bansal ; Anshul Agarwal ; Vinay Kumar Jadoun. Springer Science and Business Media Deutschland GmbH, 2022. pp. 215-221 (Lecture Notes in Electrical Engineering).

@inproceedings{accb811f22cb40e29740cdd9000a61ad,

title = "Power Transformer Summer Peak Load Prediction Using SCADA and Supervised Learning",

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.",

author = "Neeraj Kanwar and Divay Bargoti and Jadoun, \{Vinay Kumar\}",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; International Conference on Energy, Material Sciences and Mechanical Engineering, EMSME 2020 ; Conference date: 30-10-2020 Through 01-11-2020",

year = "2022",

doi = "10.1007/978-981-16-1476-7\_21",

language = "English",

isbn = "9789811614750",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "215--221",

editor = "Bansal, \{Ramesh C.\} and Anshul Agarwal and Jadoun, \{Vinay Kumar\}",

booktitle = "Advances in Energy Technology - Select Proceedings of EMSME 2020",

address = "Germany",

}

Kanwar, N, Bargoti, D & Jadoun, VK 2022, Power Transformer Summer Peak Load Prediction Using SCADA and Supervised Learning. in RC Bansal, A Agarwal & VK Jadoun (eds), Advances in Energy Technology - Select Proceedings of EMSME 2020. Lecture Notes in Electrical Engineering, vol. 766, Springer Science and Business Media Deutschland GmbH, pp. 215-221, International Conference on Energy, Material Sciences and Mechanical Engineering, EMSME 2020, New Delhi, India, 30-10-20. https://doi.org/10.1007/978-981-16-1476-7_21

Power Transformer Summer Peak Load Prediction Using SCADA and Supervised Learning. / Kanwar, Neeraj; Bargoti, Divay; Jadoun, Vinay Kumar.
Advances in Energy Technology - Select Proceedings of EMSME 2020. ed. / Ramesh C. Bansal; Anshul Agarwal; Vinay Kumar Jadoun. Springer Science and Business Media Deutschland GmbH, 2022. p. 215-221 (Lecture Notes in Electrical Engineering; Vol. 766).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Power Transformer Summer Peak Load Prediction Using SCADA and Supervised Learning

AU - Kanwar, Neeraj

AU - Bargoti, Divay

AU - Jadoun, Vinay Kumar

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

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M3 - Conference contribution

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SN - 9789811614750

T3 - Lecture Notes in Electrical Engineering

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BT - Advances in Energy Technology - Select Proceedings of EMSME 2020

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T2 - International Conference on Energy, Material Sciences and Mechanical Engineering, EMSME 2020

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ER -

Kanwar N, Bargoti D, Jadoun VK. Power Transformer Summer Peak Load Prediction Using SCADA and Supervised Learning. In Bansal RC, Agarwal A, Jadoun VK, editors, Advances in Energy Technology - Select Proceedings of EMSME 2020. Springer Science and Business Media Deutschland GmbH. 2022. p. 215-221. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-16-1476-7_21

Power Transformer Summer Peak Load Prediction Using SCADA and Supervised Learning

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