Short-Term Load Prediction Technology Based on Attention-CNN-GRU Hybrid Neural Network
The essence of power system load prediction is to predict the demand of power market,and short-term power load prediction is one of the important tasks of power sector.At present,the main load prediction methods include conven-tional prediction,grey prediction,chaos theory prediction,intelligent technology prediction,optimal combination predic-tion,etc.Among them,the most typical intelligent prediction method is artificial neural network.Artificial neural net-works form highly complex nonlinear dynamic systems.Their self-learning capability is of great significance in prediction,as they can learn from historical load data to capture the nonlinear relationships between input and output variables.Due to the various factors affecting electricity load,employing neural network algorithms in load prediction can significantly en-hance prediction accuracy.This paper tries to address the practical demand for short-term load prediction in the Ningxia power grid and proposes a short-term load prediction technique based on an attention-enhanced CNN-GRU hybrid neural network.This technology improves the CNN-GRU model by introducing an attention mechanism,effectively enhancing prediction accuracy and interpretability.Simulation experiments were conducted on the actual dataset of Ningxia Power Grid,and the results showed that the model proposed in this paper has high prediction accuracy and reliability.
short-term power predictionconvolutional neural networkgated recurrent unitattention mechanism
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