Abstract
In the operation of the power system, the prediction of reactive load can provide accurate data foundation conditions for reactive power compensation planning. Aiming at the characteristics of time series, non-linearity and small fluctuation of power reactive load data, this paper proposes a reactive power load prediction method based on LSTM (long short-term memory) neural network. The proposed method is used to process and predict the power reactive load data of a certain area in Fujian Province. The experimental results show that the LSTM model with one layer and 64 neuron structures have the best result among the support vector regression (SVR), the random forest model (RF), Recurrent Neuron Network (RNN), LSTM-CNN hybrid neural network, the prediction of reactive load on long-term scale has higher accuracy and stability.
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