Title page for etd-0808117-144320


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URN etd-0808117-144320
Author Tao-Ting Tung
Author's Email Address No Public.
Statistics This thesis had been viewed 5347 times. Download 770 times.
Department Electrical Engineering
Year 2017
Semester 1
Degree Master
Type of Document
Language zh-TW.Big5 Chinese
Title Power System Short-term Load Forecast
Date of Defense 2017-08-24
Page Count 131
Keyword
  • user interface
  • apparent temperature
  • semiparametric additive model
  • neural networks
  • short-term load forecasting
  • Abstract Accurate Short-Term Load Forecast (STLF) is important in the security constrained unit commitment and economic dispatch to achieve reliable, stable and efficient power system operations. The requirement for accurate STLF is becoming greater due to the integrations of large scale variable generations in order to maintain stable frequency and voltage in future power system operations. Based on Taiwan power system historical load data and apparent temperature data, different ANN models are developed to conduct STLF. For special and normal days. Forecast results are compared with those of the Seasonal Autoregressive Integrated Moving Average (SARIMA) model and the Semiparametric Additive Model (SAM). A user interface is used to collect data at 9:00, train the models, and predict loads in the forecasting to day. Mean absolute percent errors are computed to assess the effectiveness of the proposed models. Test results indicate that a mix of ANN and SAM provides better forecast results as compared to the other tested methods.
    Advisory Committee
  • Min-Siong Liang - chair
  • Chin-Sien Moo - co-chair
  • Kin-Cheong Sou - co-chair
  • Chan-Nan Lu - advisor
  • Files
  • etd-0808117-144320.pdf
  • indicate access worldwide
    Date of Submission 2017-09-08

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