Research on ultra-short-term load forecasting method of oil and gas field integrated energy system based on hybrid neural network

Aiming at the source-network-load coordination requirements of distributed new energy and cooling, heating and electricity loads in oil and gas fields, this paper proposes an ultra-short-term load forecasting method based on Convolutional-Bidirectional Long Short Term Memory-Skip (CNN-BiLSTM-Skip). The model extracts the spatial nonlinear characteristics of load data through convolutional layers, and the bidirectional LSTM (Long Short-Term Memory, LSTM), and CNN-LSTM (Convolutional Neural Networks-Long Short-Term Memory, CNN-LSTM) layer captures the bidirectional dependence of timing. The linear component is processed by the autoregressive layer. The output of each layer is feature fused through Skip Connection to alleviate the problem of layer disappearance and improve the training stability. Based on the architecture of a combined cooling, heating and power system in an oil and gas field (including wind power, photovoltaic, power grid, natural gas and other multi-energy coupling), a feature input mechanism is designed: the convolutional layer handles the local spatiotemporal model, the BiLSTM models the long-range time series correlation, and the jumping layer realizes the cross-layer feature transfer. Finally, the prediction results are output by linear weighting. Experiments show that compared with BP (Back Propagation, BP), LSTM, and CNN-LSTM (Convolutional Neural Networks-Long Short-Term Memory, CNN-LSTM) algorithms, the average MAPE index of this model is improved by 3.78%, 1.63%, and 0.74%, and the RMSE and MAE are the smallest; the daily operating cost of the system is reduced by ¥493.5, ¥196.5, and ¥90.2, respectively, and the wind and solar utilization rate is increased by 3.0% and 2.9%. The model provides a new technical path for the stability and economy of the integrated energy system in oil and gas fields through the fusion of heterogeneous features and the layer optimization mechanism.