Abstract：Accurate and efficient multi-load forecasting is of great significance for the operation control and

scheduling of integrated energy system（IES），in order to improve the load forecasting effect，a integrated energy

system load prediction model based on least absolute shrinkage and selection operator（LASSO）and LSTM-GRU

neural network was proposed. Firstly，in order to solve the problem of complex data caused by meteorological

factors in the integrated energy system，a big data selection and analysis algorithm based on LASSO was studied to

select and analyze the meteorological factors to obtain an effective data set. Secondly，the long short-term memory

（LSTM）neural network was used to predict the system load，and the preliminary prediction value was obtained.

Subsequently，the gated recurrent unit（GRU）was used to construct the error compensation model，and the

compensation value of the prediction error was obtained through the training and learning of the prediction error.

Finally，by reconstructing the output of the two，a more ideal prediction result was obtained. Through the simulation of the example，the proposed prediction model has higher prediction accuracy than the traditional LSTM neural network prediction model and the LSTM model optimized by particle swarm optimizer （PSO）.

      Key words：load forecasting；integrated energy system（IES）；LASSO algorithm；error compensation；

long short-term memory（LSTM）nerural network