Photovoltaic-Power Prediction Model Based on Quantum Long Short-Term Memory Network
Owing to the rapid development of new energy-generation systems，accurate photovoltaic （PV）-power forecasting is crucial in enhancing the grid’s ability to integrate solar energy. To address the insufficient accuracy of existing methods，this study proposes a quantum long short-term memory （LSTM） netw...
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Editorial Department of Electric Power Construction
2025-01-01
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| Series: | Dianli jianshe |
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| Online Access: | https://www.cepc.com.cn/fileup/1000-7229/PDF/1735120229262-1669362598.pdf |
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| author | PAN Dong, YANG Xin, SHI Tiancheng, FANG Yuan, WANG Xuli, DOU Menghan |
| author_facet | PAN Dong, YANG Xin, SHI Tiancheng, FANG Yuan, WANG Xuli, DOU Menghan |
| author_sort | PAN Dong, YANG Xin, SHI Tiancheng, FANG Yuan, WANG Xuli, DOU Menghan |
| collection | DOAJ |
| description | Owing to the rapid development of new energy-generation systems，accurate photovoltaic （PV）-power forecasting is crucial in enhancing the grid’s ability to integrate solar energy. To address the insufficient accuracy of existing methods，this study proposes a quantum long short-term memory （LSTM） network PV-power forecasting model that is more lightweight in terms of parameters，more stable in training，and yields better results. First，data decomposition is performed based on a singular spectrum analysis. Subsequently，a quantum LSTM network is constructed to capture high-dimensional data features，followed by the utilization of dual attention mechanisms to capture features and temporal importance，which culminates in results output via a decision layer. Case studies show that compared with conventional methods，quantum PV-power forecasting can effectively improve the accuracy of such forecasts. Furthermore，empirical validation underscores the feasibility and effectiveness of utilizing quantum computers for PV-power forecasting.As quantum computers continue to develop，there is hope for the future application of these systems to achieve rapid and precise forecasting of power generation from large-scale photovoltaic （PV） power stations，This would assist in the safe scheduling and reliable operation of the power grid. |
| format | Article |
| id | doaj-art-f2aefc0a7e31454c81ad1898efb1823f |
| institution | Kabale University |
| issn | 1000-7229 |
| language | zho |
| publishDate | 2025-01-01 |
| publisher | Editorial Department of Electric Power Construction |
| record_format | Article |
| series | Dianli jianshe |
| spelling | doaj-art-f2aefc0a7e31454c81ad1898efb1823f2025-02-10T02:35:53ZzhoEditorial Department of Electric Power ConstructionDianli jianshe1000-72292025-01-0146112213310.12204/j.issn.1000-7229.2025.01.011Photovoltaic-Power Prediction Model Based on Quantum Long Short-Term Memory NetworkPAN Dong, YANG Xin, SHI Tiancheng, FANG Yuan, WANG Xuli, DOU Menghan01. Economic and Technological Research Institute of State Grid Anhui Electric Power Co.，Ltd.，Hefei 230022，China;2. Original Quantum Computing Technology （Hefei） Co.，Ltd.，Hefei 231283，ChinaOwing to the rapid development of new energy-generation systems，accurate photovoltaic （PV）-power forecasting is crucial in enhancing the grid’s ability to integrate solar energy. To address the insufficient accuracy of existing methods，this study proposes a quantum long short-term memory （LSTM） network PV-power forecasting model that is more lightweight in terms of parameters，more stable in training，and yields better results. First，data decomposition is performed based on a singular spectrum analysis. Subsequently，a quantum LSTM network is constructed to capture high-dimensional data features，followed by the utilization of dual attention mechanisms to capture features and temporal importance，which culminates in results output via a decision layer. Case studies show that compared with conventional methods，quantum PV-power forecasting can effectively improve the accuracy of such forecasts. Furthermore，empirical validation underscores the feasibility and effectiveness of utilizing quantum computers for PV-power forecasting.As quantum computers continue to develop，there is hope for the future application of these systems to achieve rapid and precise forecasting of power generation from large-scale photovoltaic （PV） power stations，This would assist in the safe scheduling and reliable operation of the power grid.https://www.cepc.com.cn/fileup/1000-7229/PDF/1735120229262-1669362598.pdfquantum computer|quantum long short-term memory network|dual-stage attention|photovoltaic power prediction |
| spellingShingle | PAN Dong, YANG Xin, SHI Tiancheng, FANG Yuan, WANG Xuli, DOU Menghan Photovoltaic-Power Prediction Model Based on Quantum Long Short-Term Memory Network Dianli jianshe quantum computer|quantum long short-term memory network|dual-stage attention|photovoltaic power prediction |
| title | Photovoltaic-Power Prediction Model Based on Quantum Long Short-Term Memory Network |
| title_full | Photovoltaic-Power Prediction Model Based on Quantum Long Short-Term Memory Network |
| title_fullStr | Photovoltaic-Power Prediction Model Based on Quantum Long Short-Term Memory Network |
| title_full_unstemmed | Photovoltaic-Power Prediction Model Based on Quantum Long Short-Term Memory Network |
| title_short | Photovoltaic-Power Prediction Model Based on Quantum Long Short-Term Memory Network |
| title_sort | photovoltaic power prediction model based on quantum long short term memory network |
| topic | quantum computer|quantum long short-term memory network|dual-stage attention|photovoltaic power prediction |
| url | https://www.cepc.com.cn/fileup/1000-7229/PDF/1735120229262-1669362598.pdf |
| work_keys_str_mv | AT pandongyangxinshitianchengfangyuanwangxulidoumenghan photovoltaicpowerpredictionmodelbasedonquantumlongshorttermmemorynetwork |