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| Wavelet time series MPARIMA modeling for power system short term load forecasting | |
| 引用本文： | 冉启文,单永正,王建赜,王骐. Wavelet time series MPARIMA modeling for power system short term load forecasting[J]. 哈尔滨工业大学学报(英文版), 2003, 10(1) |
| 作者姓名： | 冉启文 单永正 王建赜 王骐 |
| 作者单位： | National Key Lab of Tunable Laser Technology Harbin Institute of Technology，Dept. of Math，Harbin Institute of Technology，Dept. of Electrical Engineering，Harbin Institute of Technology，National Key Lab of Tunable Laser Technology Harbin Institute of Technology Harbin 150001，China，Dept. of Math，Harbin Institute of Technology，Harbin 150001，China，Harbin 150001，China，Harbin 150001，China，Harbin 150001，China |
| 基金项目： | theMultidisciplineScientificResearchFoundationofHarbinInstituteofTechnology(GrantNo .HITMD .2 0 0 0 18) |
| 摘 要： | 0 ＩＮＴＲＯＤＵＣＴＩＯＮＦｏｒｔｈｅｅｃｏｎｏｍｉｃａｎｄｓｅｃｕｒｅｏｐｅｒａｔｉｏｎｏｆｐｏｗｅｒｓｙｓ ｔｅｍｓ ,ｐｒｅｄｉｃｔｉｏｎｏｆｆｕｔｕｒｅｌｏａｄｄｅｍａｎｄ ,ｉｎｐａｒｔｉｃｕｌａｒｓｈｏｒｔｔｅｒｍ (2 4ｈｏｕｒｓａｈｅａｄ)ｌｏａｄｆｏｒｅｃａｓｔｉｎｇ(ＳＴＬＦ)ｉｓｉｍｐｏｒ ｔａｎｔ .Ｆｕｎｄａｍｅｎｔａｌｏｐｅｒａｔｉｏｎａｌｆｕｎｃｔｉｏｎｓｓｕｃｈａｓｕｎｉｔｃｏｍ ｍｉｔｍｅｎｔ,ｈｙｄｒｏｔｈｅｒｍａｌｃｏｏｒｄｉｎａｔｉｏｎ ,ｉｎｔｅｒ ｃｈａｎｇｅｅｖａｌｕ… |
Wavelet time series MPARIMA modeling for power system short term load forecasting |
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| RAN Qi wen ,,SHAN Yong zheng ,WANG Jian ze ,WANG Qi. Wavelet time series MPARIMA modeling for power system short term load forecasting[J]. Journal of Harbin Institute of Technology (New Series), 2003, 10(1) | |
| Authors: | RAN Qi wen SHAN Yong zheng WANG Jian ze WANG Qi |
| Affiliation: | 1. National Key Lab of Tunable Laser Technology, Harbin Institute of Technology, Harbin 150001, China;Dept. Of Math, Harbin Institute of Technology, Harbin 150001, China 2. Dept. of Math, Harbin Institute of Technology, Harbin 150001, China 3. Dept. of Electrical Engineering, Harbin Institute of Technology, Harbin 150001, China 4. National Key Lab of Tunable Laser Technology, Harbin Institute of Technology, Harbin 150001, China |
| Abstract: | The wavelet power system short term load forecasting(STLF) uses a mulriple periodical autoregressive integrated moving average(MPARIMA) model to model the mulriple near periodicity, nonstationarity and nonlinearity existed in power system short term quarter hour load time series, and can therefore accurately forecast the quarter hour loads of weekdays and weekends, and provide more accurate results than the conventional techniques, such as artificial neural networks and autoregressive moving average(ARMA) models test results. Obtained with a power system networks in a city in Northeastern part of China confirm the validity of the approach proposed. |
| Keywords: | wavelet forecasting method short term load forecast MPARIMA model |
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