پديد آورنده :
خليل زاده، اسماعيل
عنوان :
بررسي تاثير اصلاح داده هاي بار ساعتي نامناسب در بهبود پيش بيني بار كوتاه مدت به كمك شبكه هاي عصبي
مقطع تحصيلي :
كارشناسي ارشد
محل تحصيل :
اصفهان: دانشگاه صنعتي اصفهان، دانشكده برق و كامپيوتر
صفحه شمار :
نه،127ص.:مصور،جدول،نمودار
يادداشت :
ص.ع.به فارسي و انگليسي
استاد راهنما :
اكبرابراهيمي
استاد مشاور :
محمد اسماعيل همداني گلشن
توصيفگر ها :
قدر مطلق باقيمانده نرماليزه شده
تاريخ نمايه سازي :
1/3/89
استاد داور :
غلامرضا يوسفي،محمدرضا احمدزاده
تاريخ ورود اطلاعات :
1396/09/29
دانشكده :
مهندسي برق و كامپيوتر
چكيده فارسي :
به فارسي و انگليسي: قابل رويت در نسخه ديجيتالي
چكيده انگليسي :
Analysis Impact of the Modification of Improper Load Data to Improvement Short Term Load Forecasting Based on Neural Networks Esmail Khalilzadeh e khalilzadeh@ec iut ac ir 9 1 2010 Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan 84156 83111 Iran Degree M Sc Language Persian A Ebrahimi ebrahimi@cc iut ac irAbstractShort term load forecasting is performed by data whose validation is subjected to measurement systemserrors transmission errors and in addition unpredictable events and load shedding voltage collapse leadto inappropriate load data and unusual load profiles In this thesis the significant of the refinement ofimproper load data and different schemes for omitting improper load profiles is investigated In addition ascheme based on absolute normalized residual for modification of improper hour load data instead ofomitting is proposed and for Isfahan power system a load forecasting based on these algorithm is performed And the performance of this algorithm in decreasing the STLF errors is assessed Finally the impact ofomitting and modifying of improper load data in decreasing the STLF errors in schemes based on perceptronneural networks is investigated Because of special load pattern and lock of similar load profiles loadforecasting in holidays has been always one of the challenging problems of STLF systems In additionvarious holidays and displacement of some of them and the existence of inappropriate load data lead toincreasing STLF errors In this thesis forecasting the holidays based on the Absolute Normalized Residual isperformed The results show that the load data modification in the algorithms can significantly decrease theerrors of forecasting in normal days and specially holidays KeywordsLoad Forecasting Improper Load Data Absolute Normalized Residual
استاد راهنما :
اكبرابراهيمي
استاد مشاور :
محمد اسماعيل همداني گلشن
استاد داور :
غلامرضا يوسفي،محمدرضا احمدزاده
