基于改进DaNN的综合能源系统多能负荷预测

随着能源革命的推进及双碳目标的提出，综合能源系统越发受到广大研究者的重视，对综合能源系统进行高效的规划和控制离不开精准的多能负荷预测。基于上述需求，引入迁移学习理论，提出一种改进领域自适应神经网络（DaNN）负荷预测模型对综合能源系统中的冷、热、电负荷进行统一建模与预测。首先，通过历史数据分别构筑冷、热、电负荷特征图，随后输入改进DaNN的参数共享卷积层和全连接层；其次，基于冷、热、电负荷联合预测的特点改进传统神经网络的损失函数，加入最大均值差异指标，并优化训练模型；最后，通过3个各自独立的全连接层分别输出冷、热、电负荷的预测值。通过采用实际算例验证并与基准模型对比可知，所提改进DaNN模型能够有效提高综合能源多能负荷预测精度。

Multiple energy load forecasting of integrated energy system based on improved DaNN

With the advancement of energy revolution and the proposal of two-carbon goal,the integrated energy system has been paid more and more attention by many researchers. Accurate multiple load forecasting is indispensable for efficient and correct scheduling and control of integrated energy system. Based on the above requirement,the transfer learning theory is introduced,and an improved domain adaptive neural network (DaNN) load forecasting model is proposed to unified model and forecast the cooling,heating and electrical load in the integrated energy system. Firstly,the feature pictures of multiple loads are constructed by historical data and be input into the parameter sharing layer of improved DaNN. Secondly,based on the characteristics of combined forecasting of multiple loads,the loss function of traditional neural network is improved. The maximum mean difference (MMD) index is added,and the training model is optimized. Finally,the forecast values of cooling,heating and electrical loads are output through three independent full connection layers. Through the actual case verification and comparison with the traditional model,it proves that improved DaNN model effectively improves the accuracy of multiple energy load forecasting of integrated energy system.