Presentation | 1999/12/17 Next Day Electric Peak Load Forecasting Using Two Types of Neural Networks Hiroyasu Konishi, Masanori Izumida, Kenji Murakami, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Using two types of neural networks, we have forecasted a next day electric peak load in Shikoku area. First neural network uses learning data that are past 10 days of the target day. Second neural network uses those of last year. Then we get the forecasted results that are the weighted sum of the forecasted results by the first neural network and the forecasting results by the second neural network. In addition, at the first neural network, we use the learning method, which uses learning data with priority. In the method, learning data close to the target day has high priority. Moreover, we have studied suitable input features. Using those methods, we have reduces the forecasting error. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Neural network / Electric load forecasting / Input features / Weights of input data |
Paper # | PRMU99-177 |
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Committee | PRMU |
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Conference Date | 1999/12/17(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Pattern Recognition and Media Understanding (PRMU) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Next Day Electric Peak Load Forecasting Using Two Types of Neural Networks |
Sub Title (in English) | |
Keyword(1) | Neural network |
Keyword(2) | Electric load forecasting |
Keyword(3) | Input features |
Keyword(4) | Weights of input data |
1st Author's Name | Hiroyasu Konishi |
1st Author's Affiliation | Shikoku Research Institute inc.() |
2nd Author's Name | Masanori Izumida |
2nd Author's Affiliation | Ehime univ. |
3rd Author's Name | Kenji Murakami |
3rd Author's Affiliation | Ehime univ. |
Date | 1999/12/17 |
Paper # | PRMU99-177 |
Volume (vol) | vol.99 |
Number (no) | 515 |
Page | pp.pp.- |
#Pages | 8 |
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