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

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

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 dimensionalit...

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Bibliographic Details
Main Authors: Venkataramana Veeramsetty, Arjun Mohnot, Gaurav Singal, Surender Reddy Salkuti
Format: Article
Language:English
Published: MDPI AG 2021-05-01
Series:Energies
Subjects:
dimensionality reduction
simple linear regression
multiple linear regression
polynomial regression
load forecasting
Online Access:https://www.mdpi.com/1996-1073/14/11/2981
Description
Summary: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.
ISSN:1996-1073

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