Prediction of Solar Irradiance One Hour Ahead Based on Quantum Long Short-Term Memory Network

The short-term forecasting of photovoltaic (PV) power generation ensures the scheduling and dispatching of electrical power, helps design a PV-integrated energy management system, and enhances the security of grid operation. However, due to the randomness of solar energy, the output of the PV system will fluctuate, which will affect the safe operation of the grid. To solve this problem, a high-precision hybrid prediction model based on variational quantum circuit (VQC) and long short-term memory (LSTM) network is developed to predict solar irradiance 1 hour in advance. VQC is embedded in LSTM to iteratively optimize the weight parameters of four gates (forgetting gate, input gate, cell state, and output gate) to improve prediction accuracy. To evaluate the prediction performance of this model, five solar radiation observatories located in China are selected, together with widely used models including seasonal autoregressive integrated moving average, convolution neural network, recurrent neural network (RNN), gate recurrent unit, (GRU), and LSTM; comparisons are made under different seasons and months. The experimental results show that the annual average root mean square error of the quantum long short-term memory model is 61.756 <inline-formula><tex-math notation="LaTeX">$\text{W/m}^{2}$</tex-math></inline-formula>, which is reduced by 10.7&#x0025;, 13.9&#x0025;, 8.1&#x0025;, 3.8&#x0025;, and 3.4&#x0025;, respectively, compared with other models; the annual average mean absolute error is 24.257 <inline-formula><tex-math notation="LaTeX">$\text{W/m}^{2}$</tex-math></inline-formula>, which is reduced by 28.1&#x0025;, 28.9&#x0025;, 24.1&#x0025;, 12.2&#x0025;, and 12.8&#x0025;, respectively, compared with other models; the annual average R-Square (<inline-formula><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula>) is 0.946, which is improved by 1.5&#x0025;, 1.9&#x0025;, 1.2&#x0025;, 0.4&#x0025;, and 0.4&#x0025;, respectively, compared with other models.