Short Term Active Power Load Prediction on A 33/11 kV Substation Using Regression Models

Venkataramana Veeramsetty, Arjun Mohnot, Gaurav Singal and Surender Reddy Salkuti
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Venkataramana Veeramsetty: Center for Artificial Intelligence and Deep Learning, Department of Electrical and Electronics Engineering, S R Engineering College, Warangal 506371, India
Arjun Mohnot: Department of Computer Science Engineering, Bennett University, Greater Noida 201310, India
Gaurav Singal: Department of Computer Science Engineering, Bennett University, Greater Noida 201310, India
Surender Reddy Salkuti: Department of Railroad and Electrical Engineering, Woosong University, Daejeon 34606, Korea

Energies, 2021, vol. 14, issue 11, 1-21

Abstract: Electric power load forecasting is an essential task in the power system restructured environment for successful trading of power in energy exchange and economic operation. In this paper, various regression models have been used to predict the active power load. Model optimization with dimensionality reduction has been done by observing correlation among original input features. Load data has been collected from a 33/11 kV substation near Kakathiya University in Warangal. The regression models with available load data have been trained and tested using Microsoft Azure services. Based on the results analysis it has been observed that the proposed regression models predict the demand on substation with better accuracy.

Keywords: dimensionality reduction; simple linear regression; multiple linear regression; polynomial regression; load forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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