Distributed Photovoltaic Ultra-short-term Power Forecasting Method Based on Temporal Analog Matching Approach and Transformer Network Modeling
To address the challenge of low prediction accuracy of distributed photovoltaic (PV) power generation under sudden weather change scenarios due to the lack of meteorological data, this paper proposes a distributed PV ultra-short-term power prediction method based on temporal analog matching approach...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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State Grid Energy Research Institute
2024-12-01
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| Series: | Zhongguo dianli |
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| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202403112 |
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| _version_ | 1850070534480461824 |
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| author | Pengwei YANG Liping ZHAO Junfa CHEN Zhao ZHEN Fei WANG Liming LI |
| author_facet | Pengwei YANG Liping ZHAO Junfa CHEN Zhao ZHEN Fei WANG Liming LI |
| author_sort | Pengwei YANG |
| collection | DOAJ |
| description | To address the challenge of low prediction accuracy of distributed photovoltaic (PV) power generation under sudden weather change scenarios due to the lack of meteorological data, this paper proposes a distributed PV ultra-short-term power prediction method based on temporal analog matching approach (TAMA) and Transformer network modeling. Firstly, the concept of similar time periods is extended from days to more flexible time periods, and a matching strategy integrating historical power and satellite remote sensing information is proposed to efficiently identify the most critical time periods of similar power for prediction without relying on meteorological data. Based on this, the powerful temporal modeling capability of the Transformer network is used to dynamically resolve the hidden correlations in multi-source similar time periods, and deeply mine the key features of power, thus providing more accurate ultra-short-term power prediction for distributed PV systems under sudden weather change conditions. Finally, the effectiveness of the proposed method is verified through actual distributed PV power generation data. |
| format | Article |
| id | doaj-art-a7fcf7e7167a47488aa8d69b885ef302 |
| institution | DOAJ |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2024-12-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-a7fcf7e7167a47488aa8d69b885ef3022025-08-20T02:47:32ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492024-12-015712607010.11930/j.issn.1004-9649.202403112zgdl-57-12-yangpengweiDistributed Photovoltaic Ultra-short-term Power Forecasting Method Based on Temporal Analog Matching Approach and Transformer Network ModelingPengwei YANG0Liping ZHAO1Junfa CHEN2Zhao ZHEN3Fei WANG4Liming LI5Zhangjiakou Power Supply Company, State Grid Jibei Electric Power Co., Ltd., Zhangjiakou 075000, ChinaZhangjiakou Power Supply Company, State Grid Jibei Electric Power Co., Ltd., Zhangjiakou 075000, ChinaBeijing Power Transmission and Distribution Co., Ltd., Beijing 102401, ChinaDepartment of Power Engineering, North China Electric Power University, Baoding 071003, ChinaDepartment of Power Engineering, North China Electric Power University, Baoding 071003, ChinaBeijing Tsingdian Technology Co., Ltd., Beijing 100190, ChinaTo address the challenge of low prediction accuracy of distributed photovoltaic (PV) power generation under sudden weather change scenarios due to the lack of meteorological data, this paper proposes a distributed PV ultra-short-term power prediction method based on temporal analog matching approach (TAMA) and Transformer network modeling. Firstly, the concept of similar time periods is extended from days to more flexible time periods, and a matching strategy integrating historical power and satellite remote sensing information is proposed to efficiently identify the most critical time periods of similar power for prediction without relying on meteorological data. Based on this, the powerful temporal modeling capability of the Transformer network is used to dynamically resolve the hidden correlations in multi-source similar time periods, and deeply mine the key features of power, thus providing more accurate ultra-short-term power prediction for distributed PV systems under sudden weather change conditions. Finally, the effectiveness of the proposed method is verified through actual distributed PV power generation data.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202403112distributed pv powersimilar time periodstransformer modelultra-short-term power forecastingsatellite remote sensing information |
| spellingShingle | Pengwei YANG Liping ZHAO Junfa CHEN Zhao ZHEN Fei WANG Liming LI Distributed Photovoltaic Ultra-short-term Power Forecasting Method Based on Temporal Analog Matching Approach and Transformer Network Modeling Zhongguo dianli distributed pv power similar time periods transformer model ultra-short-term power forecasting satellite remote sensing information |
| title | Distributed Photovoltaic Ultra-short-term Power Forecasting Method Based on Temporal Analog Matching Approach and Transformer Network Modeling |
| title_full | Distributed Photovoltaic Ultra-short-term Power Forecasting Method Based on Temporal Analog Matching Approach and Transformer Network Modeling |
| title_fullStr | Distributed Photovoltaic Ultra-short-term Power Forecasting Method Based on Temporal Analog Matching Approach and Transformer Network Modeling |
| title_full_unstemmed | Distributed Photovoltaic Ultra-short-term Power Forecasting Method Based on Temporal Analog Matching Approach and Transformer Network Modeling |
| title_short | Distributed Photovoltaic Ultra-short-term Power Forecasting Method Based on Temporal Analog Matching Approach and Transformer Network Modeling |
| title_sort | distributed photovoltaic ultra short term power forecasting method based on temporal analog matching approach and transformer network modeling |
| topic | distributed pv power similar time periods transformer model ultra-short-term power forecasting satellite remote sensing information |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202403112 |
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