Electricity Load Forecasting Using Data Mining Technique
Accurate load forecasting is become crucial in power system operation and planning; both for deregulated and regulated electricity market.A variety of methods including neural networks, time series, hybrid method and fuzzy logic have been developed for load forecasting. The time series techniques...
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| Main Author: | |
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| Format: | Book Section |
| Language: | English |
| Published: |
InTech
2012
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| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/8091/1/book_chapter_data_mining.pdf http://eprints.utem.edu.my/id/eprint/8091/ |
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| Summary: | Accurate load forecasting is become crucial in power system operation and planning; both for deregulated and regulated electricity market.A variety of methods including neural networks, time series, hybrid method and
fuzzy logic have been developed for load forecasting. The time series techniques have been widely used because load behavior can be analyzed in a time series signal with
hourly, daily, weekly, and seasonal periodicities. However, for a huge power system covering large geographical area such as Peninsular Malaysia, a single forecasting model for the entire Malaysia would not satisfy the forecasting accuracy; due to the load and weather diversity. Thus, this research will cater these conditions whereby five models of SARIMA (Seasonal ARIMA) Time Series were developed for five day types. |
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