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)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.
quantum computerquantum long short-term memory networkdual-stage attentionphotovoltaic power prediction
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