关键词: 综合能源系统, 负荷预测, 卷积神经网络, 双向长短期记忆网络, 核密度估计, 随机森林, 碳价

Abstract:

To address the challenges of insufficient multi-source heterogeneous data fusion and uncertainty quantification in load forecasting for integrated energy systems， a hybrid CNN-BiLSTM-RF-KDE model was proposed. The model utilized convolutional neural network（CNN） to extract local features of load data， bidirectional long short-term memory （BiLSTM） to capture bidirectional temporal dependencies， random forest（RF） to handle high-dimensional nonlinear relationships， and kernel density estimation（KDE） to quantify prediction uncertainty. Additionally， an electricity-heat-gas multi-energy flow coupling model was established to analyze the influence of different carbon price intervals on scheduling strategies. Case studies demonstrated that the coefficient of determination（R²） for electrical and heating load forecasting reached 0.93 and 0.96 on the training set， and 0.79 and 0.84 on the test set， respectively. The predicted power generation and heat output of each device closely aligned with the mean trend， indicating that the model provided more accurate load predictions. Based on this data， more reliable analysis and scheduling of integrated energy systems could be achieved.

Key words: integrated energy system, load forecasting, convolutional neural network, bidirectional long short-term memory network, kernel density estimation, random forest, carbon price