Optimizing building energy performance predictions: A comparative study of artificial intelligence models

The December 2022 Commercial Buildings Energy Consumption Survey conducted by the Energy Information Administration (EIA) found that space heating constitutes 32% of total building end-use energy, with cooling accounting for 9%. It has a significant impact on climate change. In this research, intelligent models were developed to predict the annual heating and cooling loads (HL and CL) of residential buildings. Eight inputs, including relative compactness, roof area, overall height, surface area, glazing area, wall area, glazing area distribution, and orientation, were used for the modeling development. The artificial intelligence (AI) models were Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Random Forest (RF), Multi-layer Perceptron (MLP), Gradient Boosting (GBoost), and Extreme Gradient Boosting (XGBoost). Three scenarios of input combinations were tested: Scenario-1 (S1) with eight inputs and Scenario-2 (S2) with five inputs. Similarly, Scenario-3 (S3) with five inputs. Results indicated that, the RF was the superior algorithm in HL for S1, achieving Kling-Gupta Efficiency (KGE = 0.998) and Root Mean Square Error (RMSE = 0.501 kW h/m²). XGBoost performed outstandingly in CL with KGE = 0.994 and RMSE = 0.922 kW h/m². In S2, KNN showed excellent HL with KGE = 0.945 and RMSE = 3.094 kW h/m², and RF outperformed in CL with KGE = 0.941 and RMSE = 2.727 kW h/m². In S3, XGBoost exhibited the highest efficiency for HL with KGE = 0.997 and RMSE = 0.492 kW h/m², while RF performed best for CL with KGE = 0.976 and RMSE = 1.686 kW h/m². In conclusion, S2 proved to be a logical choice, matching the efficiency of S1 with reduced error. Overall, HL predictions generally displayed superior performance compared to CL predictions.