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Comparison Study of Neural Network Methods for Electricity Forecasting
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 이상열 | - |
dc.contributor.author | 김정애 | - |
dc.date.accessioned | 2017-07-19T08:48:29Z | - |
dc.date.available | 2017-07-19T08:48:29Z | - |
dc.date.issued | 2017-02 | - |
dc.identifier.other | 000000142053 | - |
dc.identifier.uri | https://hdl.handle.net/10371/131341 | - |
dc.description | 학위논문 (석사)-- 서울대학교 대학원 : 통계학과, 2017. 2. 이상열. | - |
dc.description.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. | - |
dc.description.tableofcontents | 1 Introduction 1
2 Models 4 2.1 Nerual networks 4 2.2 Neural network with global ARMA models 5 2.3 Neural network with local ARMA models 6 3 Simulation Studies 9 4 Post-sample Forecasting 13 5 Concluding remarks 17 Bibliography 18 국문초록 22 | - |
dc.format | application/pdf | - |
dc.format.extent | 2705143 bytes | - |
dc.format.medium | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | 서울대학교 대학원 | - |
dc.subject | Load forecasting | - |
dc.subject | Load management | - |
dc.subject | Neural networks | - |
dc.subject | ARMA model | - |
dc.subject.ddc | 519 | - |
dc.title | Comparison Study of Neural Network Methods for Electricity Forecasting | - |
dc.type | Thesis | - |
dc.description.degree | Master | - |
dc.citation.pages | 17 | - |
dc.contributor.affiliation | 자연과학대학 통계학과 | - |
dc.date.awarded | 2017-02 | - |
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