Remote resource transmission load prediction based on Stacking ensemble learning
The traditional load forecasting method for remote resource transmission in power grid neglects the ensemble learning of resources,which leads to a large deviation between the load forecasting result and the actual value.Therefore,a load forecasting method for remote resource transmission based on Stacking ensemble learning is proposed.The Stacking ensemble learning model is constructed,meanwhile,the Stac-king-LSTM network hybrid model is constructed through the long short-term memory network.The influen-cing factor data characteristic diagram is constructed by using the time sliding window,which would be then input into the network hybrid model.The training is realized by using the Stacking basic learning training layer,and the training results are input into the LSTM network layer to complete the load forecasting of pow-er grid remote resource transmission.The experiment results show that the network convergence speed of this method is fast,the contribution of features obtained is high,and the load forecasting results are close to the actual values,which can better track the load changes.
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