Abstract: The distribution network connected to high permeability distributed photovoltaic should reduce the load of distribution network to a certain extent. Due to the difference of the coupling characteristics of the load, photovoltaic output and meteorological factors in the distribution network, and the strong randomness, it is difficult and randomness to predict the net load of the distribution network. In order to realize the short-term prediction of the net load of the fluctuating distribution network, the short-term prediction model of the neural network is constructed based on the long-short term memory (LSTM). The short-term prediction model of photovoltaic output and the hourly load prediction model of distribution network are built by LSTM, and cross validation is used to optimize the structure super parameters of each LSTM predictor. The net load of distribution network is obtained by comparing the predicted results. From the analysis of the experimental results, it can be seen that the LSTM method can adaptively mine the correlation between photovoltaic output characteristics and historical load forecasting objects. Compared with the support vector regression (SVR) method, this method has high prediction accuracy and simple process.

Key words: load, distribution network, photovoltaic, prediction, LSTM