| Title |
Prediction Model of Indoor Temperature Distribution for Optimal Control of Building Energy |
| Authors |
Oh Ik Kwon ; Young Il Kim ; Sean Hay Kim |
| DOI |
https://doi.org/10.6110/KJACR.2021.33.3.130 |
| Keywords |
건물에너지; 최적화; 예측; 디지털트윈; 실내온도분포; 가상센서 Building energy; Optimization; Prediction; Digital twin; Indoor temperature distribution; Virtual sensor |
| Abstract |
Quantification of building energy performance is required for diagnosis, prediction, and evaluation of energy efficient use and performance improvement of buildings. Implementing a resistance capacity (RC) model, this study presents a hydrodynamic physical model that can predict the distribution of room temperatures with sensors of minimum quantity. The proposed prediction model could predict indoor thermal behavior very similarly, even though it did not link with other models or apply optimization techniques during the verification process. Under conditions not affected by thermodynamic parameters, the root mean square error (RMSE) range for predicting air temperature of other spaces was 0.115℃ to 0.357℃ with an average indoor air temperature of 0.132℃. Predictive models with simple input conditions developed in this study could be integrated with other various models and used for optimal control of building energy. |