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Electrical load forecasting using time series model
시계열 모형을 이용한 일일 전력 피크 예측

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Authors
박재신
Advisor
조신섭
Major
자연과학대학 통계학과
Issue Date
2015-02
Publisher
서울대학교 대학원
Keywords
Transfer function modelIntervention analysisDiscomfort in- dexSensory temperature
Description
학위논문 (석사)-- 서울대학교 대학원 : 통계학과, 2015. 2. 조신섭.
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
Language
English
URI
https://hdl.handle.net/10371/131294
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College of Natural Sciences (자연과학대학)Dept. of Statistics (통계학과)Theses (Master's Degree_통계학과)
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