Short Term Load Forecasting With Time Series Analysis
Load forecasting is vitally important for the electric industry in the deregulated economy. It has many applications including energy purchasing and generation, load switching, contract evaluation, and infrastructure development. A large variety of mathematical methods have been developed for loa...
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| Format: | Monograph |
| Published: |
UTeM
2010
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| Online Access: | http://library.utem.edu.my:8000/elmu/index.jsp?module=webopac-d&action=fullDisplayRetriever.jsp&szMaterialNo=0000061406 http://library.utem.edu.my:8000/elmu/index.jsp?module=webopac-d&action=fullDisplayRetriever.jsp&szMaterialNo=0000061406 http://eprints.utem.edu.my/3659/1/Short_Term_Load_Forecasting_With_Time_Series_Analysis_-___TK1005_.C77_2010.pdf http://eprints.utem.edu.my/3659/2/Short_Term_Load_Forecasting_With_Time_Series_Analysis_-___TK1005_.C77_2010.pdf |
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| Summary: | Load forecasting is vitally important for the electric industry in the deregulated
economy. It has many applications including energy purchasing and generation, load
switching, contract evaluation, and infrastructure development. A large variety of
mathematical methods have been developed for load forecasting. Short-term load forecasting
plays an important role in electric power system operation and planning. An accurate load
forecasting not only reduces the generation cost in a power system, but also provides a good
principle of effective operation. In this project, the Autoregressive Integrated Moving Average
(ARIMA) of Time-Series model will be applied to the short-term load forecasting for the
Peninsular Malaysia load data. ARIMA is a practical forecasting method in the electric shortterm
load forecasting fields for linear prediction. The choice of the forecasting model becomes
the important factor to improve load forecasting accuracy. The aim of this project is to achieve
forecasting error that is equal or less than 1.5% using Minitab and XLSTAT statistical
software. The data collected is 7 weeks of half an hourly load data for Peninsular Malaysia. |
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