Improving the Model for Energy Consumption Load Demand Forecasting
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- Bunnoon Pituk
- Electrical Engineering Department, Engineering Faculty, Prince of Songkla University
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- Chalermyanont Kusumal
- Electrical Engineering Department, Engineering Faculty, Prince of Songkla University
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- Limsakul Chusak
- Electrical Engineering Department, Engineering Faculty, Prince of Songkla University
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This paper proposes an application of a filter method in preprocessing stage for mid-term load demand forecasting to improve electricity load forecasting and to guarantee satisfactory forecasting accuracy. Case study employs the historical electricity consumption demand data in Thailand which were recorded in the 12 years of 1997 through to 2007. The load demand forecasted value is used for unit commitment and fuel reserve planning in the power system. This method consists of a trend component and a cyclical component decomposed from the original load demand using the Hodrick-Prescott (HP) filter in the preprocessing stage and the forecasting of each component using Double Neural Networks (DNNs) in the forecasting stage. Experimental results show that with preprocessing before forecasting can predict the load demand better than that without preprocessing.
収録刊行物
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- 電気学会論文誌B(電力・エネルギー部門誌)
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電気学会論文誌B(電力・エネルギー部門誌) 132 (3), 235-243, 2012
一般社団法人 電気学会
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詳細情報 詳細情報について
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- CRID
- 1390282679577825920
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- NII論文ID
- 10030525757
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- NII書誌ID
- AN10136334
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- BIBCODE
- 2012IJTPE.132..235B
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- ISSN
- 13488147
- 03854213
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- NDL書誌ID
- 023555542
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
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- CiNii Articles
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- 使用不可