Short-term photovoltaic power forecasting based on hybrid quantum gated recurrent unit

Abstract

Photovoltaic power generation forecasting is crucial for energy management, smart grid construction, and energy markets. This study proposes a hybrid quantum–classical gated recurrent unit (HQGRU)-based framework for forecasting short-term photovoltaic power generation in a time-series manner. The HQGRU model uses a classical layer followed by a quantum embedding circuit to convert classical data into quantum data. Subsequently, variational quantum circuits are used for feature extraction. To demonstrate the performance of the proposed model, we used practical data on photovoltaic power generation and the weather in Busan, Republic of Korea. The results demonstrate the high accuracy of the proposed HQGRU model.