Title page for etd-0625117-102422


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URN etd-0625117-102422
Author Song-yi Guo
Author's Email Address No Public.
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Department Applied Mathematics
Year 2016
Semester 2
Degree Master
Type of Document
Language English
Title Short-term Load Forecasting with Model Averaging
Date of Defense 2017-06-09
Page Count 52
Keyword
  • mean absolute percentage errors
  • hybrid estimation method
  • semi-parametric regression model
  • proportional estimation method
  • B-spline functions
  • Abstract Accuracy of electricity load forecasting plays an important role for electric utilities and regulators. Overestimation of electricity load demand will cause waste on energy. On the other hand, underestimation may cause the electrical power off due to lack of power supply. In this work, we investigate two forecasting methods, one works directly with the original load observed at each time period, the other works with daily total and corresponding proportions on each time period. We also use model averaging method to combine the two forecasts. The forecasting results will be evaluated by the mean absolute percentage errors (MAPE), peak absolute percentage errors (Peak APE), valley absolute percentage errors (Valley APE) for the three years (2013-2015) on the Singapore load data, where for the forecasting day and the next seven days, based on four weeks training data before the forecasting day, are provided to demonstrate the effectiveness of the newly proposed model averaging short-term load forecasting methodology. As Loads are affected by the national holidays, we will discuss different ways to do the analysis for the special days.
    Advisory Committee
  • Mei-Hui Guo - chair
  • Chung Chang - co-chair
  • Fu-Chuen Chang - co-chair
  • Ray-Bing Chen - co-chair
  • Mong-Na Lo - advisor
  • Files
  • etd-0625117-102422.pdf
  • Indicate in-campus at 5 year and off-campus access at 5 year.
    Date of Submission 2017-07-26

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