Power load forecasting method based on feature selection strategy and TCN
Due to the influence of many external factors,power load has great fluctuation and randomness,which makes it very difficult to predict the load with high accuracy.In order to effectively deal with high-di-mensional features and improve prediction accuracy,a power load prediction method based on feature selec-tion strategy and Temporal Convolutional Network(TCN)is proposed.Firstly,the feature selection strategy based on eXtreme Gradient Boosting(XGBoost)is adopted to deeply mine the features with strong correla-tion with load as the input of the prediction model.Secondly,a power load prediction model based on TCN is constructed to predict the load data after feature selection.Finally,the real load data of a city is used for simulation analysis,and the results show that the proposed method has higher prediction accuracy than the traditional prediction method.
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