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By improving the accuracy of wind power prediction, reliable data support is provided for the Economic Dispatch(ED) model of Electric and Heat Integrated Energy System(EHIES) based on wind power consumption. In this paper, Convolutional Neural Network(CNN), Bidirectional Long Short Term Memory (BI-LSTM) and Attention Mechanism(AM) are used for wind power prediction. For the input of the CNN-BI-LSTM-AM model, the intrinsic mode signals obtained through Variational Mode Decomposition(VMD) and multidimensional power time series are used. Taking the October data of a coastal wind farm in Liaoning Province as an example, the accuracy of the CNN-BI-LSTM-AM model for wind power prediction reached 97.81%, which is better than the traditional model. Exploring the influence of wind power prediction accuracy on economy, wind power utilization efficiency, and carbon emissions in Two-stage robust optimization(TRO) and deterministic optimization models for EHIES. The EHIES model proposed in this paper considers the deterministic and uncertain economic dispatch of wind power under the situation of conventional heat load and heat load demand when the temperature drops suddenly. By solving the deterministic scheduling model and uncertain two-stage robust optimal scheduling model under different wind power forecasting accuracy, the electric heating integrated energy system can obtain the scheduling scheme with the lowest system operation cost, the highest wind power utilization and the less carbon emissions under the "universal" and "worst" scenarios, respectively. The simulation results show that compared with the traditional CPLEX solver, the C&#x0026;CG algorithm can solve the main problem and sub problem alternately based on the column constraint generation algorithm and the strong duality theory. It can obtain the optimal solution of the economic dispatch model, providing an important reference for the optimal economic dispatch and wind power consumption strategy of the EHIES with wind power access.

Research on Collaborative Optimal Dispatching of Electric Heating Integrated Energy Based on Wind Power Prediction Accuracy