DATA REDUCTION VERSUS FEATURE SELECTION IN APPLICATION OF DAILY MAXIMUM POWER LOAD FORECASTING

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DATA REDUCTION VERSUS FEATURE SELECTION IN APPLICATION OF DAILY MAXIMUM POWER LOAD FORECASTING

Journal Title: Informatyka Automatyka Pomiary w Gospodarce i Ochronie Środowiska - Year 2013, Vol 3, Issue 2

Abstract

Load forecasting task of small energetic region is a difficult problem due to high variability of power consumption. The accurate forecast of the power in the next hours is very important from the economic point of view. The most important problems in prediction are the choice of predictor and selection of features. Two methods of features selection was presented – simple selection using of genetic algorithm and dimensionality reduction methods for creating new features from many available measured data. As a predictor the Support Vector Machine working in regression mode (SVR) was chosen. The load forecasting results with SVR are presented and discussed.

Authors and Affiliations

Krzysztof Siwek

Keywords

prognozowanie obciążeń elektroenergetycznych redukcja wymiarowości algorytm genetyczny maszyna wektorów podtrzymujących load power forecasting Dimensionality Reduction genetic algorithm support vector machine

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EP ID EP59442
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