Distributed Photovoltaic Power Interval Prediction Based on Spatio-Temporal Correlation Feature and B-LSTM Model
A distributed photovoltaic (PV) power interval prediction method based on spatio-temporal correlation features and bayesian long short-term memory (B-LSTM) model is proposed. The approximate Bayesian neural network is constructed by adding a Dropout layer based on the LSTM neural network to establis...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
| Published: |
State Grid Energy Research Institute
2024-07-01
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| Series: | Zhongguo dianli |
| Subjects: | |
| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202310049 |
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| Summary: | A distributed photovoltaic (PV) power interval prediction method based on spatio-temporal correlation features and bayesian long short-term memory (B-LSTM) model is proposed. The approximate Bayesian neural network is constructed by adding a Dropout layer based on the LSTM neural network to establish a B-LSTM model considering spatio-temporal correlation features, and its powerful memory and feature extraction capabilities are used to extract deep features for distributed PV power interval prediction for intrinsic mode function components with different feature scales. An arithmetic example is analysed with an actual distributed PV dataset in a region to verify the superiority of the proposed method. |
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| ISSN: | 1004-9649 |