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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| Format: | Article |
| Language: | zho |
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State Grid Energy Research Institute
2024-07-01
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| Series: | Zhongguo dianli |
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| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202310049 |
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| author | Haijun WANG Rongrong JU Yinghua DONG |
| author_facet | Haijun WANG Rongrong JU Yinghua DONG |
| author_sort | Haijun WANG |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-85e73f01a7984e7d8b4d313d295de835 |
| institution | DOAJ |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2024-07-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-85e73f01a7984e7d8b4d313d295de8352025-08-20T02:56:45ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492024-07-01577748010.11930/j.issn.1004-9649.202310049zgdl-57-04-wanghaijunDistributed Photovoltaic Power Interval Prediction Based on Spatio-Temporal Correlation Feature and B-LSTM ModelHaijun WANG0Rongrong JU1Yinghua DONG2Nanjing Vocational Institute of Railway Technology, Nanjing 210031, ChinaChina Electric Power Research Institute, Nanjing 210003, ChinaChina Electric Power Research Institute, Nanjing 210003, ChinaA 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.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202310049distributed photovoltaicsspatio-temporal correlationinterval prediction |
| spellingShingle | Haijun WANG Rongrong JU Yinghua DONG Distributed Photovoltaic Power Interval Prediction Based on Spatio-Temporal Correlation Feature and B-LSTM Model Zhongguo dianli distributed photovoltaics spatio-temporal correlation interval prediction |
| title | Distributed Photovoltaic Power Interval Prediction Based on Spatio-Temporal Correlation Feature and B-LSTM Model |
| title_full | Distributed Photovoltaic Power Interval Prediction Based on Spatio-Temporal Correlation Feature and B-LSTM Model |
| title_fullStr | Distributed Photovoltaic Power Interval Prediction Based on Spatio-Temporal Correlation Feature and B-LSTM Model |
| title_full_unstemmed | Distributed Photovoltaic Power Interval Prediction Based on Spatio-Temporal Correlation Feature and B-LSTM Model |
| title_short | Distributed Photovoltaic Power Interval Prediction Based on Spatio-Temporal Correlation Feature and B-LSTM Model |
| title_sort | distributed photovoltaic power interval prediction based on spatio temporal correlation feature and b lstm model |
| topic | distributed photovoltaics spatio-temporal correlation interval prediction |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202310049 |
| work_keys_str_mv | AT haijunwang distributedphotovoltaicpowerintervalpredictionbasedonspatiotemporalcorrelationfeatureandblstmmodel AT rongrongju distributedphotovoltaicpowerintervalpredictionbasedonspatiotemporalcorrelationfeatureandblstmmodel AT yinghuadong distributedphotovoltaicpowerintervalpredictionbasedonspatiotemporalcorrelationfeatureandblstmmodel |