Use Repeated Linear Least Squares Method to Estimate The Parameters of The Output Error Model (OE) With a Practical Application
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Abstract
This research has examined the study of the output error model (OE) and knowledge of the difference in the daily consumption of electric energy in Baghdad Governorate for the month (May, June and July) for the year 2019 and its relationship to heat. And the prediction of electrical energy for the month of (August) and the extent of its conformity with the original values according to the studied data. Where the model was built using several traditional phases, which is the estimation using repeated linear least squares method and diagnosis. The model rank was determined using the Akaik's Final Prediction Error Criteria and predicting the average daily consumption of electric energy by fifteen (15) days forward, The data were obtained from the Ministry of Electricity/Department of Operation and Control .
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أولاً. المصادر العربية:
وزارة الكهـرباء العراقية، دائرة التشغيل والتحكم لعام 2019.
ثانياً. المصادر الاجنبية:
Aghilone Graziella & Cavacece Massimo, (2013), Identification of Behavior of Anti-Vibration Gloves by ARMAX Model, Progress in Vibration and Acoustics, September 2013, Volume 1, Issue 1, PP. [1-10].
Brunot Mathieu & Janot Alexandre & Carrillo Francisco & Cheong, Joono & Noël, Jean-Philip, (2019), Output Error Methods for Robot Identification, Journal of Dynamic Systems, Measurement, and Control, 142 (3), ISSN 0022-0434.
Dong Shijian & Liu Tao & Chen Fengwei, (2016), Output Error Model Identification Against Unexpected Load Disturbance, Institute of Advanced Control Technology, Dalian University of Technology, Dalian, 116024, P. R. China.
Ljung Lennart, (1999), System Identification: Theory for the User 2 edition, Linkoping University sweden, Prentice Hall International.
Mantalos P. & Mattheou K, & Karagrigoriou A., (2008), Using the Divergence Information Criterion for the Determination of the Order of an Autoregressive Process, [email protected], University of Lund Sweden and University of Cyprus Cyprus.
Nelles Oliver, (2001), Nonlinear System Identification, springer-verlag 1st edition.
Rangasamg Kirutigaa, (2014), System Identification for SISO Systems, university Teknologi Petronas.
Soderstrom Torsten & Stoica Peter, (2001), System Identification, Prentice Hall International.
Sridhar Rahulram, (2012), Electromechanical actuater bearing fault detection using empirically extracted features, Rochester institute of technology RIT scholar worhs.
Wahlberg Bo & Annergren Mariette & R. Rojas Cristian, (2011), on Optimal Input Signal Design for Identification of Output Error Models, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden.
ثالثاً. المصادر الالكترونية:
www.uobabylon.edu.iq/eprints/pubdoc_1_2138_1418.docx