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Conceptual and validation study for short term load forecasting approach realizing smart grid energy management / Amr Ahmed Aydarous Mohamed ; Supervised Mohamed A. Moustafa Hassan , Mostafa A. Elshahed

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Amr Ahmed Aydarous Mohamed , 2018Description: 208 P. : charts , facsimiles ; 25cmOther title:
  • دراسة مبدأ وتحقق طريقة للتنبؤ بالأحمال عل ىالمدى القصير محققاً لنظام التحكم بالطاقه فى الشبكات الذكية [Added title page title]
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Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Electrical Power and Machines Summary: The energy sector is one of the major sectors where big steps can be taken towards a sustainable future. This thesis represents a study to determine the most effective and accurate technique for Short Term Load Forecasting (STLF) that can be done through Integrated Intelligent Energy Management processes for Smart Grid. The Load Forecasting shall be based on the historical data only. This study compared the results of 56 different scenarios in the number of inputs, different algorithms, and input method. ANN, PSO, MAACPSO, and ANFIS were used as different algorithms. Different input methods include a new proposed method which considers Multi Stages Forecasting. Study verification is done by testing versus other practical STLF methods using traditional inputs.The proposed method results are auspicious with reduced Mean Absolute Error
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Item type Current library Home library Call number Copy number Status Date due Barcode
Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.07.M.Sc.2018.Am.C (Browse shelf(Opens below)) Not for loan 01010110077108000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.07.M.Sc.2018.Am.C (Browse shelf(Opens below)) 77108.CD Not for loan 01020110077108000

Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Electrical Power and Machines

The energy sector is one of the major sectors where big steps can be taken towards a sustainable future. This thesis represents a study to determine the most effective and accurate technique for Short Term Load Forecasting (STLF) that can be done through Integrated Intelligent Energy Management processes for Smart Grid. The Load Forecasting shall be based on the historical data only. This study compared the results of 56 different scenarios in the number of inputs, different algorithms, and input method. ANN, PSO, MAACPSO, and ANFIS were used as different algorithms. Different input methods include a new proposed method which considers Multi Stages Forecasting. Study verification is done by testing versus other practical STLF methods using traditional inputs.The proposed method results are auspicious with reduced Mean Absolute Error

Issued also as CD

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