Thermal dynamics and energy consumption prediction of buildings based on TCN-LSTM-Attention
FU Wei-hao
ZHAO Qian-chuan
Abstract：Based on the multi-variable coupling characteristics of the thermal dynamics and energy consumption of the HVAC system and the problem of insufficient data prediction accuracy,this paper proposes a fusion prediction model integrating time convolution-long short-term memory-attention mechanism(TCN-LSTM-Attention).Firstly,in order to better capture the short-term and long-term dependencies in the building operation data,a TCN-LSTM-Attention building thermal dynamics and energy consumption prediction model is established to predict HVAC energy consumption,indoor temperature,and PMV.The improved particle swarm optimization(IPSO)algorithm is used to optimize the hyperparam-eters of the prediction model,reduce the prediction error of the model,and analyze the model's approximation ability.Secondly,the EnergyPlus is used to build a building simulation model for verification.The prediction model is verified by using the operation data of an office building in Hebei Province.The experiments show that this model has better prediction accuracy and prediction stability compared with the comparison algorithms,and the generalization of the algorithm when the building envelope parameters change is verified.
Keywords：building condition predictiontemporal convolutional networklong short term memoryattentionparticle swarm optimization algorithm
Publication Date：2025-11-30
Online Publishing Date：2025-12-29(First online date of this platform, not the publication date of the document)
Pages：11( 2125-2135 )