Temporal Convolutional Neural Network-Based Cold Load Prediction for Large Office Buildings
Affiliation of Author(s):
建筑设备科学与工程学院
Journal:
Journal of Thermal Science and Engineering Applications
Key Words:
large office building, cold load forecast, gray relation analysis, temporal
convolutional neural network, improved black widow optimization algorithm, energy
efficiency, energy systems
Abstract:
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.
Paper Publications
