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Published in:

Volume 5 Issue 9
September-2018
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR1809621


Registration ID:
189034

Page Number

756-760

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Title

An Effective Multiple Linear Regression Model For Power Load Prediction

Abstract

Predictive analysis is the one of the major machine learning application. In this paper, we compare machine Learning Regression methods to propose an efficient model for predicting net hourly power output of the combined cycle power plant. The power load of a power plant is effected by the parameters atmospheric pressure, humidity, and exhaust steam pressure, ambient temperature.These four parameters are taken as input parameters for the proposed model.Based on these parameters,we analyze different multiple linear regression machine learning methods and proposed an efficient model which gives better prediction of energy output of the power plant.In the proposed model,we applied forward selection, backward elimination techniques and implement a machine learning model which has lower standard error rate. The implementation was done in Python Language, which provides vast number of packages for machine learning algorithms.

Key Words

Predictive analysis, power output, Multiple Linear Regression, Python

Cite This Article

"An Effective Multiple Linear Regression Model For Power Load Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 9, page no.756-760, September-2018, Available :http://www.jetir.org/papers/JETIR1809621.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"An Effective Multiple Linear Regression Model For Power Load Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 9, page no. pp756-760, September-2018, Available at : http://www.jetir.org/papers/JETIR1809621.pdf

Publication Details

Published Paper ID: JETIR1809621
Registration ID: 189034
Published In: Volume 5 | Issue 9 | Year September-2018
DOI (Digital Object Identifier):
Page No: 756-760
Country: kakinada, AP, India .
Area: Science & Technology
ISSN Number: 2349-5162
Publisher: IJ Publication


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