کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
380343 1437439 2015 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Hybrid filter–wrapper feature selection for short-term load forecasting
ترجمه فارسی عنوان
انتخاب فیلتر انتخاب ترکیبی برای پیش بینی بار کوتاه مدت
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی


• We propose a filter–wrapper feature selection method for STLF.
• PMI is first used to filter irrelevant and redundant features.
• A wrapper method is then used to further reduce the remaining redundant features.
• The proposed hybrid method can identify less inputs with relatively shorter time.
• Better forecasting results are obtained based on the selected features.

Selection of input features plays an important role in developing models for short-term load forecasting (STLF). Previous studies along this line of research have focused pre-dominantly on filter and wrapper methods. Given the potential value of a hybrid selection scheme that includes both filter and wrapper methods in constructing an appropriate pool of features, coupled with the general lack of success in employing filter or wrapper methods individually, in this study we propose a hybrid filter–wrapper approach for STLF feature selection. This proposed approach, which is believed to have taken full advantage of the strengths of both filter and wrapper methods, first uses the Partial Mutual Information based filter method to filter out most of the irrelevant and redundant features, and subsequently applies a wrapper method, implemented via a firefly algorithm, to further reduce the redundant features without degrading the forecasting accuracy. The well-established support vector regression is selected as the modeler to implement the proposed hybrid feature selection scheme. Real-world electricity load datasets from a North-American electric utility and the Global Energy Forecasting Competition 2012 have been used to test the performance of the proposed approach, and the experimental results show its superiority over selected counterparts.

A flowchart of the proposed hybrid feature selection method.Figure optionsDownload as PowerPoint slide

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Engineering Applications of Artificial Intelligence - Volume 40, April 2015, Pages 17–27
نویسندگان
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