所属单位：建筑设备科学与工程学院

发表刊物：Journal of Thermal Science and Engineering Applications

关键字：large office building, cold load forecast, gray relation analysis, temporal convolutional neural network, improved black widow optimization algorithm, energy efficiency, energy systems

摘要：n heating, ventilation, and air conditioning (HVAC) systems for large office buildings, accurate cooling load prediction facilitates the elaboration of energy-efficient and energy-saving operation strategies for the system. In this paper, a hybrid prediction model based on gray relational analysis-improved black widow optimization algorithmtemporal convolutional neural network (GRA-IBWOA-TCN) is proposed for cold load prediction of large office buildings. First, the factors influencing cold load in large office buildings were analyzed, with GRA used to identify key features and reduce input data dimensionality for the prediction model. Second, three improvement strategies are proposed to enhance optimization performance at different stages of the black widow optimization algorithm, aimed at establishing a prediction model for optimizing TCN hyper-parameters through IBWOA. Finally, the algorithm optimization and prediction model comparison experiments were conducted with the intra-week dataset (T1) and the weekend dataset (T2) of a large office building as the study samples, respectively. The results show that the mean absolute percentage error values of the GRA-IBWOA-TCN model for the prediction results of the T1 and T2 datasets are 0.581% and 0.348%, respectively, which are 81.1% and 88.3% lower compared to the TCN model, and exhibit the highest prediction accuracy in optimizing the results of the TCN model and the prediction models, such as backpropagation, support vector machine, long short-term memory, and convolutional neural network, with multiple algorithms, good stability, and generalization ability. In summary, the hybrid prediction model proposed in this paper can provide effective technical support for the energy-saving management of HVAC systems in large office buildings.

第一作者：冯增喜

论文类型：期刊论文

卷号：16(11): 111010

是否译文：否

发表时间：2024-09-24