Short Term Load Forecasting Based Artificial Neural Network

Present study develops short term electric load forecasting using neural network; based on historical series of power demand the neural network chosen for this network is feed forward network, this neural network has five input variables ( hour of the day, the day of the week, the load for the previ...

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Main Author: Adel M. Dakhil
Format: Article
Language:English
Published: College of Engineering, University of Basrah 2014-06-01
Series:Iraqi Journal for Electrical and Electronic Engineering
Subjects:
Online Access:http://ijeee.org/volums/volume10/IJEEE10PDF/paper105.pdf
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author Adel M. Dakhil
author_facet Adel M. Dakhil
author_sort Adel M. Dakhil
collection DOAJ
description Present study develops short term electric load forecasting using neural network; based on historical series of power demand the neural network chosen for this network is feed forward network, this neural network has five input variables ( hour of the day, the day of the week, the load for the previous hour, the load of the pervious day, the load for the previous week). Short term load forecast is very important due to accurate for power system operation and analysis system security among other mandatory function. The trained artificial neural network shows good accuracy and robust in forecasting future load demands for the daily operation, mean absolute percentage error (MAPE) was calculated and it is maximum value is 0.75% in load forecasting on Monday.
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spelling doaj.art-ae80ecc7fbe04a65b30dcf0a2fa055302022-12-22T03:22:50ZengCollege of Engineering, University of BasrahIraqi Journal for Electrical and Electronic Engineering1814-58922078-60692014-06-011014247Short Term Load Forecasting Based Artificial Neural NetworkAdel M. Dakhil0Department of Electrical Engineering Misan University Iraq- MisanPresent study develops short term electric load forecasting using neural network; based on historical series of power demand the neural network chosen for this network is feed forward network, this neural network has five input variables ( hour of the day, the day of the week, the load for the previous hour, the load of the pervious day, the load for the previous week). Short term load forecast is very important due to accurate for power system operation and analysis system security among other mandatory function. The trained artificial neural network shows good accuracy and robust in forecasting future load demands for the daily operation, mean absolute percentage error (MAPE) was calculated and it is maximum value is 0.75% in load forecasting on Monday.http://ijeee.org/volums/volume10/IJEEE10PDF/paper105.pdfActual loadFeed forwardLoad forecastingNeural Networkpredicted loadShort term load forecasting
spellingShingle Adel M. Dakhil
Short Term Load Forecasting Based Artificial Neural Network
Iraqi Journal for Electrical and Electronic Engineering
Actual load
Feed forward
Load forecasting
Neural Network
predicted load
Short term load forecasting
title Short Term Load Forecasting Based Artificial Neural Network
title_full Short Term Load Forecasting Based Artificial Neural Network
title_fullStr Short Term Load Forecasting Based Artificial Neural Network
title_full_unstemmed Short Term Load Forecasting Based Artificial Neural Network
title_short Short Term Load Forecasting Based Artificial Neural Network
title_sort short term load forecasting based artificial neural network
topic Actual load
Feed forward
Load forecasting
Neural Network
predicted load
Short term load forecasting
url http://ijeee.org/volums/volume10/IJEEE10PDF/paper105.pdf
work_keys_str_mv AT adelmdakhil shorttermloadforecastingbasedartificialneuralnetwork