What question did this study set out to answer?

This research aims to develop an effective model for predicting building cooling loads to improve energy efficiency.

January 21, 2026Energies0 citationsOpen Access

Building Cooling Load Prediction Based on GWO-CNN-LSTM

XZXuelong ZhangQingdao University CZChao ZhangQingdao University YMYongzhi MaQingdao University

Key Points

This research aims to develop an effective model for predicting building cooling loads to improve energy efficiency.
Developed a 3D building model using SketchUp.
Simulated cooling load with EnergyPlus and OpenStudio.
Utilized Grey Wolf Optimizer to tune hyperparameters of the CNN-LSTM model.
Conducted a comparative analysis with baseline models such as LSTM, GA-CNN-LSTM, and PSO-CNN-LSTM.
Achieved an R2 of 0.9266.
Reported MAE of 218.7830 W and RMSE of 327.4012 W.
Demonstrated 35.0% improvement in MAE and 27.0% in RMSE compared to LSTM.
Showed 20.8% improvement in MAE and 16.3% in RMSE compared to GA-CNN-LSTM.

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.

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