کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
400252 1438795 2008 6 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Forecasting electricity load with optimized local learning models
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Forecasting electricity load with optimized local learning models
چکیده انگلیسی

Electricity load forecasting plays an important role in the strategy management of electricity power system. Learning methods such as artificial neural networks, and more recently, support vector regression machines (SVR) have been introduced to this field. In practices we often expect a fast forecasting, while standard algorithms based on the whole data set are time consuming. To this end, in this paper we introduce local learning strategy considering only a subset of candidates in the neighborhood of the test point and present a general form for it. Concretely, we consider the combination of KNN and εε-SVR for its powerful generalization ability and simplicity. As for model optimizing, Pattern Search method is used for model selection and multiple kernels are developed to improve the performance. Intensive experiments on a real world electricity load forecasting have been carried out and the results show that our methods can improve the performance at a reduced computation cost. Consequently local learning strategy provides a promising alternative for fast electricity load forecasting.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Electrical Power & Energy Systems - Volume 30, Issue 10, December 2008, Pages 603–608
نویسندگان
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