电力建设2025，Vol.46Issue(1)：122-133,12.DOI:10.12204/j.issn.1000-7229.2025.01.011
基于量子长短期记忆网络的光伏功率预测模型
Photovoltaic-Power Prediction Model Based on Quantum Long Short-Term Memory Network
摘要
Abstract
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.关键词
量子计算机/量子长短期记忆网络/双重注意力/光伏功率预测Key words
quantum computer/quantum long short-term memory network/dual-stage attention/photovoltaic power prediction分类
信息技术与安全科学引用本文复制引用
潘东,杨欣,施天成,方圆,王绪利,窦猛汉..基于量子长短期记忆网络的光伏功率预测模型[J].电力建设,2025,46(1):122-133,12.基金项目
This work is supported by National Key R&D Program of China(No.2023YFB4502500)and State Grid Anhui Electric Power Co.,Ltd.Science and Technology Project(No.B31209230006). 国家重点研发计划项目(2023YFB4502500) （No.2023YFB4502500）
国网安徽省电力有限公司科技项目(B31209230006) （B31209230006）
