TY - GEN
T1 - Power Transformer Summer Peak Load Prediction Using SCADA and Supervised Learning
AU - Kanwar, Neeraj
AU - Bargoti, Divay
AU - Jadoun, Vinay Kumar
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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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U2 - 10.1007/978-981-16-1476-7_21
DO - 10.1007/978-981-16-1476-7_21
M3 - Conference contribution
AN - SCOPUS:85113369270
SN - 9789811614750
T3 - Lecture Notes in Electrical Engineering
SP - 215
EP - 221
BT - Advances in Energy Technology - Select Proceedings of EMSME 2020
A2 - Bansal, Ramesh C.
A2 - Agarwal, Anshul
A2 - Jadoun, Vinay Kumar
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Energy, Material Sciences and Mechanical Engineering, EMSME 2020
Y2 - 30 October 2020 through 1 November 2020
ER -