Accurate Estimation Method of Customer Baseline Load for Continuous Participation of Industrial Users in Demand Response
A computational method combining K-means cluster analysis with long- and short-term memory neural network algorithm is proposed, and transfer learning is carried out by industrial homogeneous group information to further optimize the estimation effect. Accurate estimation of industrial customer powe...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
State Grid Energy Research Institute
2024-03-01
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| Series: | Zhongguo dianli |
| Subjects: | |
| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202312036 |
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| Summary: | A computational method combining K-means cluster analysis with long- and short-term memory neural network algorithm is proposed, and transfer learning is carried out by industrial homogeneous group information to further optimize the estimation effect. Accurate estimation of industrial customer power baseline load under long-term continuous response is realized, and the accuracy of the demand response effect evaluation of industrial customers is improved. The effectiveness of the method is verified by the load data of industrial customers participating in demand response practice collected by the city-level virtual power plant platform. |
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| ISSN: | 1004-9649 |