SHERP

Comparison Study of Neural Network Methods for Electricity Forecasting

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Authors
김정애
Advisor
이상열
Major
자연과학대학 통계학과
Issue Date
2017
Publisher
서울대학교 대학원
Keywords
Load forecastingLoad managementNeural networksARMA model
Description
학위논문 (석사)-- 서울대학교 대학원 : 통계학과, 2017. 2. 이상열.
Abstract
Load forecasting has an important meaning in economic and secure operation of power systems. So, numerous methods are proposed to enhance the accuracy of load forecasting. In this paper, we introduce these methods and proposes two methods that combines neural networks and time series model. First one is neural networks with global ARMA model and the other one is neural networks with local ARMA model which uses moving window. And then we compares the ordinary artificial neural network model and our proposed models by simulation studies and load demand data from France. Since load demand data has trend and seasonality, we use differenced data to fit the ARMA model. Finally, we compares the forecasted values up to 24 hours to see the accuracy of each models.
Language
English
URI
http://hdl.handle.net/10371/131341
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College of Natural Sciences (자연과학대학)Dept. of Statistics (통계학과)Theses (Master's Degree_통계학과)
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