Year: 2026
Type: article
Abstract: Accurate prediction of building cooling load is crucial for enhancing energy efficiency and optimizing the operation of Heating, Ventilation, and Air Conditioning (HVAC) systems. To improve predictive accuracy, we propose a hybrid Grey Wolf Optimizer-Convolutional Neural Network–Long Short-Term Memory (GWO-CNN-LSTM) prediction model. A 3D model of the building was first developed using SketchUp, and its cooling load was subsequently simulated with EnergyPlus and OpenStudio. The Grey Wolf Optimizer (GWO) algorithm is employed to automatically tune the hyperparameters of the CNN-LSTM model, thereby improving both training efficiency and predictive performance. A comparative analysis with other models demonstrates that the proposed model effectively captures both long-term temporal patterns and short-term fluctuations in cooling load, outperforming baseline models such as Long Short-Term Memory (LSTM), Genetic Algorithm-Convolutional Neural Network-Long Short-Term Memory (GA-CNN-LSTM), and Particle Swarm Optimization-Convolutional Neural Network–Long Short-Term Memory (PSO-CNN-LSTM). A comparative analysis with other models demonstrates that the proposed model effectively captures both long-term temporal patterns and short-term fluctuations in cooling load, outperforming baseline models such as LSTM, GA-CNN-LSTM, and PSO-CNN-LSTM. The GWO-CNN-LSTM model achieves an R2 of 0.9266, with MAE and RMSE of 218.7830 W and 327.4012 W, respectively, representing improvements of 35.0% and 27.0% in MAE and RMSE compared to LSTM, and 20.8% and 16.3% compared to GA-CNN-LSTM.
Source: Energies
DOI: 10.3390/en19020498
Authors: Xuelong Zhang, Chao Zhang, Yongzhi Ma, Kunyu Liu
Institution: Qingdao University
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7124828588