Data-driven approach to predicting the energy performance of residential buildings using minimal input data. (15th April 2022)

• Record Type: Journal Article
• Authors: Seo, Jihyun; Kim, Seohoon; Lee, Sungjin; Jeong, Hakgeun; Kim, Taeyeon; Kim, Jonghun
• Is Part Of: Building and environment. Volume 214(2022)
• Publication Date: 2022-04-15
• Subjects: Data-driven approach -- Building energy modeling -- Energy prediction -- Machine learning -- Energy efficiency retrofit -- Heating energy demand; Buildings -- Environmental engineering -- Periodicals; Building -- Research -- Periodicals; Constructions -- Technique de l'environnement -- Périodiques; Electronic journals; 696
• DOI: 10.1016/j.buildenv.2022.108911
• Languages: English
• ISSNs: 0360-1323

Abstract

Abstract: To achieve carbon neutrality, the South Korean government has been retrofitting existing buildings to reduce their energy consumption. However, existing buildings often lack sufficient information for building energy modeling. In this study, a model was developed for predicting heating energy demand using only information obtained from a preliminary survey. Three different models were considered: multiple linear regression (MLR), artificial neural network (ANN), and support vector regression (SVR). They were then trained with data on old houses of low-income households in South Korea and were used to predict the heating energy demand of individual household units. Different input variables were applied to the initial models to identify target variables and tune the hyperparameters. In tests, ANN was slightly more accurate than SVR. SVR required a shorter total running time (training and prediction), but ANN was 10 times faster than SVR when only prediction was considered. Therefore, ANN was selected. The selected model method takes 0.215 s for 10, 000 cases. On the other hand, the previous method takes approximately an hour for one case except time for moving to a field. This shows the suggested method is much faster than the previous one. The proposed model was applied to a case study, and the predicted and true values had a relative error of only 1.40%. The proposed model can be used to predict the heating energy demand of old houses while requiring only the heating area and construction year as inputs. Highlights: The purpose is to predict the energy demand of old houses with limited information. Input variables were selected to reduce work steps using data-driven approaches. This study considered MLR, ANN, and SVR, and ANN was the optimal model. Using the developed ANN model can save time and labor. The suggested model can be applied to an un-tact method.