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
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spelling doaj-c447c25122ca4785bc75fcd6ca1d1cc02021-06-01T00:41:12ZengMDPI AGEnergies1996-10732021-05-01142981298110.3390/en14112981Short Term Active Power Load Prediction on A 33/11 kV Substation Using Regression ModelsVenkataramana Veeramsetty0Arjun Mohnot1Gaurav Singal2Surender Reddy Salkuti3Center for Artificial Intelligence and Deep Learning, Department of Electrical and Electronics Engineering, S R Engineering College, Warangal 506371, IndiaDepartment of Computer Science Engineering, Bennett University, Greater Noida 201310, IndiaDepartment of Computer Science Engineering, Bennett University, Greater Noida 201310, IndiaDepartment of Railroad and Electrical Engineering, Woosong University, Daejeon 34606, KoreaElectric 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.https://www.mdpi.com/1996-1073/14/11/2981dimensionality reductionsimple linear regressionmultiple linear regressionpolynomial regressionload forecasting
collection DOAJ
language English
format Article
sources DOAJ
author Venkataramana Veeramsetty
Arjun Mohnot
Gaurav Singal
Surender Reddy Salkuti
spellingShingle Venkataramana Veeramsetty
Arjun Mohnot
Gaurav Singal
Surender Reddy Salkuti
Short Term Active Power Load Prediction on A 33/11 kV Substation Using Regression Models
Energies
dimensionality reduction
simple linear regression
multiple linear regression
polynomial regression
load forecasting
author_facet Venkataramana Veeramsetty
Arjun Mohnot
Gaurav Singal
Surender Reddy Salkuti
author_sort Venkataramana Veeramsetty
title Short Term Active Power Load Prediction on A 33/11 kV Substation Using Regression Models
title_short Short Term Active Power Load Prediction on A 33/11 kV Substation Using Regression Models
title_full Short Term Active Power Load Prediction on A 33/11 kV Substation Using Regression Models
title_fullStr Short Term Active Power Load Prediction on A 33/11 kV Substation Using Regression Models
title_full_unstemmed Short Term Active Power Load Prediction on A 33/11 kV Substation Using Regression Models
title_sort short term active power load prediction on a 33/11 kv substation using regression models
publisher MDPI AG
series Energies
issn 1996-1073
publishDate 2021-05-01
description 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.
topic dimensionality reduction
simple linear regression
multiple linear regression
polynomial regression
load forecasting
url https://www.mdpi.com/1996-1073/14/11/2981
work_keys_str_mv AT venkataramanaveeramsetty shorttermactivepowerloadpredictionona3311kvsubstationusingregressionmodels
AT arjunmohnot shorttermactivepowerloadpredictionona3311kvsubstationusingregressionmodels
AT gauravsingal shorttermactivepowerloadpredictionona3311kvsubstationusingregressionmodels
AT surenderreddysalkuti shorttermactivepowerloadpredictionona3311kvsubstationusingregressionmodels
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