Abstract：The output of photovoltaic power generation shows strong volatility, which affects the dispatching management of power system. In this paper, a photovoltaic power prediction model using the hybrid of K-means++ and convolutional neural network and long short-term memory network (K- CNN-LSTM) is proposed. Firstly, the historical data set is classified by K-means++, then the appropriate data set is selected as the training set. Secondly, the LSTM prediction model with historical power as input is built to obtain the predicted power value to be modified. Then, the nonlinear relationship between meteorological parameters and photovoltaic power is mined by CNN, which is applied to obtain the correction coefficient for predicted power to improve the prediction accuracy. Finally, the random fluctuation of the input parameters is predicted for multiple times based on the point prediction model and the prediction error set is obtained to achieve interval prediction. Through the data set of photo- voltaic power station of Australian Desert Knowledge Solar Energy Center (DKASC), LSTM, CNN- LSTM and K-LSTM algorithms are selected for comparison. The results demonstrate that this method has high prediction accuracy and stability, and also achieves accurate output power interval prediction.
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2021, Vol. 22 
