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Electrical load forecasting using time series model : 시계열 모형을 이용한 일일 전력 피크 예측
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 조신섭 | - |
dc.contributor.author | 박재신 | - |
dc.date.accessioned | 2017-07-19T08:45:24Z | - |
dc.date.available | 2017-07-19T08:45:24Z | - |
dc.date.issued | 2015-02 | - |
dc.identifier.other | 000000025366 | - |
dc.identifier.uri | https://hdl.handle.net/10371/131294 | - |
dc.description | 학위논문 (석사)-- 서울대학교 대학원 : 통계학과, 2015. 2. 조신섭. | - |
dc.description.abstract | Many forecast models such as regression, exponential smoothing method,
fuzzy regression, multilayer perception and extreme learning machine have been proposed to forecast daily electrical load. But some of the models do not incorporate the autocorrelation structure and they are not easy to interpret the forecast results. In this paper, we introduced transfer function and intervention model using discomfort index, sensory temperature index as input time series and seasonal eect, sandwich day(the day is between two holidays) eect as intervention. This model allows us to interpret predictive value and to forecast more accurately. This model might be quite useful to save power cost and to supply electricity smoothly | - |
dc.description.tableofcontents | 1 Introduction 1
2 Models 3 2.1 Related research . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.2 Transfer function model . . . . . . . . . . . . . . . . . . . . . . 5 2.3 Intervention analysis . . . . . . . . . . . . . . . . . . . . . . . . 9 3 Real data analysis 11 3.1 Data description . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.2 Model tting . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.3 Forecast result . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 4 Conclusion and further discussion 22 Reference 24 ii | - |
dc.format | application/pdf | - |
dc.format.extent | 2184494 bytes | - |
dc.format.medium | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | 서울대학교 대학원 | - |
dc.subject | Transfer function model | - |
dc.subject | Intervention analysis | - |
dc.subject | Discomfort in- dex | - |
dc.subject | Sensory temperature | - |
dc.subject.ddc | 519 | - |
dc.title | Electrical load forecasting using time series model | - |
dc.title.alternative | 시계열 모형을 이용한 일일 전력 피크 예측 | - |
dc.type | Thesis | - |
dc.description.degree | Master | - |
dc.citation.pages | ii, 27 | - |
dc.contributor.affiliation | 자연과학대학 통계학과 | - |
dc.date.awarded | 2015-02 | - |
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