Please use this identifier to cite or link to this item: https://repository.sustech.edu/handle/123456789/19306
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dc.contributor.authorHUSSAIN, AL HUSSAIN MUHAMMAD AL HASSAN AL-
dc.contributor.authorDAFALLA, DAFALLA ELRASHEED ELGAILY-
dc.contributor.authorABAAS, OSAAMA ABAAS MUHAMMAD-
dc.contributor.authorIDRIS, SHAIKH IDRIS JAMAL ALDEEN AL SHAIKH-
dc.contributor.authorSupervisor-, Mohammad Osman Hassan-
dc.date.accessioned2017-12-10T07:21:23Z-
dc.date.available2017-12-10T07:21:23Z-
dc.date.issued2017-10-01-
dc.identifier.citationHUSSAIN, AL HUSSAIN MUHAMMAD AL HASSAN AL .Short-Term Load Forecasting Using Artificial Neural Network Technique/AL HUSSAIN MUHAMMAD AL HASSAN AL HUSSAIN...{etal};Mohammad Osman Hassan.-Khartoum: Sudan University of Science and Technology , College of Engineering , 2017.-56 p. :ill;28cm.- Bachelors search.en_US
dc.identifier.urihttp://repository.sustech.edu/handle/123456789/19306-
dc.descriptionBachelors searchen_US
dc.description.abstractThis project focused on short-term load forecasting [STLF] in power system operations. Load forecasting is future demand prediction, which assumes an essential part ofpower system management. Short term load forecasting [STLF] provides load predictionhelps in generation scheduling, maintenance, and unit commitment decisions. Therefore, [STLF] plays significant role in power system planning, and the performance of the economic system. This project deal with most power ful Artificial Intelligent [AI] whichis Artificial Neural Network [ANN], ANN model designed and compared with one of the statistical methods, which is time series model. MATLAB SIMULINK software is used to accomplish ANN model.This model used Multilayer Feed Forward ANN using MatlabR2016b NN-Tool is trained and examined using data of period from (1/7/2014 to 31/7/2014) . At the end, both methods shows that the STLF using artificial neural network [ANN] more accuratethan the statistical techniqueen_US
dc.description.sponsorshipSudan University of Science and Technologyen_US
dc.language.isoenen_US
dc.publisherSudan University of Science and Technologyen_US
dc.subjectNeural Networken_US
dc.subjectArtificial Neural Networken_US
dc.subjectShort-Term Load Forecastingen_US
dc.titleShort-Term Load Forecasting Using Artificial Neural Network Techniqueen_US
dc.title.alternativeالتنبؤ بالأحمال قصيرة المدى باستخدام تقنية الشبكات العصبية الاصطناعيةen_US
dc.typeThesisen_US
Appears in Collections:Bachelor of Engineering

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