Short-term Tie-line Power Prediction Based on CNN-LSTM

Abstract

Wind power output has randomness and volatility, which is easy to cause frequency and tie-line power fluctuation of interconnected power grid, and even lead to tie-line out of limit, affecting grid assessment. For this reason, this paper proposes a short-term tie-line power prediction method based on CNN-LSTM, which inputs historical tie-line power into a convolutional neural network (CNN),extracts the data features, generates the feature map, and inputs them into the long-short term memory network (LSTM) for tie-line power prediction. The proposed method is applied to predict tie-line power of a certain regional power grid. The results indicate that the prediction result of the method presented is close to the real power data and has higher prediction accuracy than the traditional prediction method.