Title page for etd-0802118-175759


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URN etd-0802118-175759
Author Yuan-Yu Hsu
Author's Email Address No Public.
Statistics This thesis had been viewed 5350 times. Download 177 times.
Department Electrical Engineering
Year 2018
Semester 1
Degree Master
Type of Document
Language zh-TW.Big5 Chinese
Title A Study on Power System Short-Term Load Forecasting
Date of Defense 2018-08-30
Page Count 129
Keyword
  • day-ahead electricity market
  • neural networks
  • short-term load forecasting
  • load curve adjustment method
  • Abstract Accurate Short-Term Load Forecast (STLF) is important in ensuring efficient and reliable power system operations. In this study, we propose a two-stage Artificial Neural Network (ANN) model for STLF. The first stage models forecast next seven days 24-hour load profiles and the second stage ANN forecasts the daily maximum peak demand and minimum off-peak demand with higher accuracy. The daily maximum and minimum loads forecasted in the second stage are used to improve the load profile obtained in the first stage. The accuracy of the proposed forecast model is tested using the historical data obtained from Taiwan Power Company (TPC). Forecast results are compared with those obtained from Semi-parametric Additive Model, Mix model, and Recurrent Neural Network model. Mean absolute percent errors are computed to assess the effectiveness of each model. Test results show that the proposed two-stage ANN model can outperform a previously proposed single stage ANN load forecast model.
    Advisory Committee
  • Chin-Chung Wu - chair
  • Le-Ren Chang-Chien - co-chair
  • Jen-Hao Teng - co-chair
  • Chan-nan Lu - advisor
  • Files
  • etd-0802118-175759.pdf
  • indicate access worldwide
    Date of Submission 2018-09-03

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