Reliable photovoltaic(PV) forecasting can provide important data support for power system operation, which is the key to realize the large-scale consumption of solar energy resources. PV forecasting task becomes crucial to ensure power system stability and economic operation. This paper reviews the existing research of PV forecasting methods from the perspective of multi-temporal scale and multi-spatial scale. Firstly, according to the forecasting process, demand, temporal and spatial scale, the forecasting methods are classified and the evaluation indicators involved in the research are listed. Secondly, based on the temporal scale of PV power generation, the results are combed through the three kind of scale of ultra-short-term, short-term and medium and long-term prediction. Thirdly, on each kind of temporal scale, the results are subdivided into single-site prediction and regional prediction to sort out in detail. Finally, the results are analyzed on the basis of the predicted temporal scale, spatial scale and input data. It has been observed that most recent papers highlight the importance of short-term predictions. The machine learning method shows excellent nonlinear description ability in short-term prediction, the prediction results are satisfactory. The spatial average effect of regional prediction reduces the variability of solar energy, the prediction results are reliable.
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 W/m 2 , which is reduced by 10.7%, 13.9%, 8.1%, 3.8%, and 3.4%, respectively, compared with other models; the annual average mean absolute error is 24.257 W/m 2 , which is reduced by 28.1%, 28.9%, 24.1%, 12.2%, and 12.8%, respectively, compared with other models; the annual average R-Square (R 2 ) is 0.946, which is improved by 1.5%, 1.9%, 1.2%, 0.4%, and 0.4%, respectively, compared with other models. INDEX TERMSLong short-term memory (LSTM) network, quantum neural network, solar irradiance forecasting, variational quantum circuit (VQC). Engineering uantum Transactions on IEEE Yu et al.: PREDICTION OF SOLAR IRRADIANCE ONE HOUR AHEAD BASED ON QUANTUM LSTM NETWORK
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