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Demand Forecasting in Deregulated Electricity Markets

by Anamika, Niranjan Kumar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 108 - Number 3
Year of Publication: 2014
Authors: Anamika, Niranjan Kumar
10.5120/18889-0171

Anamika, Niranjan Kumar . Demand Forecasting in Deregulated Electricity Markets. International Journal of Computer Applications. 108, 3 ( December 2014), 10-15. DOI=10.5120/18889-0171

@article{ 10.5120/18889-0171,
author = { Anamika, Niranjan Kumar },
title = { Demand Forecasting in Deregulated Electricity Markets },
journal = { International Journal of Computer Applications },
issue_date = { December 2014 },
volume = { 108 },
number = { 3 },
month = { December },
year = { 2014 },
issn = { 0975-8887 },
pages = { 10-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume108/number3/18889-0171/ },
doi = { 10.5120/18889-0171 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:42:01.181565+05:30
%A Anamika
%A Niranjan Kumar
%T Demand Forecasting in Deregulated Electricity Markets
%J International Journal of Computer Applications
%@ 0975-8887
%V 108
%N 3
%P 10-15
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A day ahead demand forecasting is essential for the efficient operation of electricity companies in the competitive electricity markets. Both the power producers and consumer needs single compact and robust demand forecasting tool for the efficient power system planning and execution. This research work proposes a day ahead short term demand forecasting for the competitive electricity markets using Artificial Neural Networks (ANNs). Historical demand data are collected for the month of January 2014 from PJM electricity markets. The work proposes the approach to reduce prediction error for electricity demands and aims to enhance the accuracy of next day electricity demand forecasting. Two types of demand forecasting models: classical forecasting and correlation forecasting models are proposed, explained and checked against each other. Proposed models are applied on real world case, PJM electricity markets for forecasting the demand on weekly working day, weekly off day and weekly middle day. The Mean Absolute Percentage Error (MAPE) for the two proposed models in the three respective cases is evaluated and analyzed. Results present that with all respects a day ahead demand forecasting through the correlation model are best and suitable for PJM electricity markets and produce less error with comparison of other classical models.

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Index Terms

Computer Science
Information Sciences

Keywords

Artificial Neural Networks (ANNs) Forecasting Electricity Markets Correlation

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