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그린리모델링 사전진단을 위한 실내온도 센서기반 건물에너지 예측 모델
권오익(Oh Ik Kwon),이인혜(In Hye Lee),연창근(Chang kun Yeon),김영일(Young Il Kim) 대한설비공학회 2022 대한설비공학회 학술발표대회논문집 Vol.2022 No.6
The building energy model for energy efficiency of existing buildings must be simple and accurate. In general, white box models take a lot of time and money to obtain parameter information, but nevertheless, the analysis results differ from the actual energy consumption. The black box model is widely used because it is estimated based on actual performance data of the building and provides excellent accuracy. Linear regression is a popular method among them. The method of linear regression of outdoor temperature and energy use has been frequently used for a long time. However, this approach requires a lot of accumulated data. Also, it is not possible to consider the indoor environment of the building. To improve this problem, propose a delta-T-based building energy model using a indoor temperature sensor. The proposed model had better energy usage prediction performance than other models using outdoor temperatures, even considering systematic errors in sensors. It plans to further verify the model after the green remodeling of the target building is completed. The proposed model is available in the following situations: (1) A Comparative Analysis of Energy Efficiency Levels in the Operating Stage of Existing Buildings (2) Quantitative Analysis of Energy Performance Improvement Effect of Existing Buildings (3) Support for real-time indoor environment and building energy management monitoring in connection with low-cost sensor network for buildings without building energy management system
권오익(Oh Ik Kwon),엄태윤(Tae Yun Aum),김영일(Young Il Kim) 대한설비공학회 2022 대한설비공학회 학술발표대회논문집 Vol.2022 No.11
In this study, a machine learning model for predicting building energy consumption was compared and analyzed. The models to be compared are Multiple Linear Regression (MLR), Support Vector Machine (SVM), Gaussian Process Regression (GPR), Artificial Neural Networks (ANN), and Decision Tree (DT). As a result of the analysis, ANN showed the best prediction performance with RMSE 0.212. GPR was RMSE 0.213, showing similar but slightly lower predictive performance. MLR and DT, which are easy to model, showed RMSE 0.320 and 0.284, respectively. ANN, GPR, and SVM, which are relatively difficult to model, showed better predictive performance than MLR and DT, which are relatively easy to model. It is confirmed that the predictive performance of the machine learning model is influenced by the dataset and does not provide an understanding of the predictive process, but can work effectively for predicting building energy consumption. Furthermore, in order to expand the usability of the engineering aspect of the machine learning model, it is necessary to have a building energy model with a structure that enables the convenience of model creation and use, and selection and handling of physical parameters.
그린리모델링 사전진단을 위한 실내온도 센서기반 건물에너지 예측 모델
권오익(Oh Ik Kwon),이인혜(In Hye Lee),연창근(Chang kun Yeon),김영일(Young Il Kim) 대한설비공학회 2022 대한설비공학회 학술발표대회논문집 Vol.2022 No.6
The building energy model for energy efficiency of existing buildings must be simple and accurate. In general, white box models take a lot of time and money to obtain parameter information, but nevertheless, the analysis results differ from the actual energy consumption. The black box model is widely used because it is estimated based on actual performance data of the building and provides excellent accuracy. Linear regression is a popular method among them. The method of linear regression of outdoor temperature and energy use has been frequently used for a long time. However, this approach requires a lot of accumulated data. Also, it is not possible to consider the indoor environment of the building. To improve this problem, propose a delta-T-based building energy model using a indoor temperature sensor. The proposed model had better energy usage prediction performance than other models using outdoor temperatures, even considering systematic errors in sensors. It plans to further verify the model after the green remodeling of the target building is completed. The proposed model is available in the following situations: (1) A Comparative Analysis of Energy Efficiency Levels in the Operating Stage of Existing Buildings (2) Quantitative Analysis of Energy Performance Improvement Effect of Existing Buildings (3) Support for real-time indoor environment and building energy management monitoring in connection with low-cost sensor network for buildings without building energy management system
열분석을 통한 열전용 보일러동 실내배관의 동파 가능성 예측
임병익(Byoung Ik Lim),정광섭(Kwang Seop Chung),김영일(Young Il Kim) 한국지열·수열에너지학회 2012 한국지열에너지학회논문집 Vol.8 No.3
In a heat only boiler system of a steam power plant, outdoor air required for combustion is made to pass through indoor space for increasing the boiler efficiency. Due to heat generated by various equipments, temperature of the air that enters the boiler will increase resulting in combustion efficiency. If the outdoor air temperature is low, however, this will cause freezing and bursting of pipes which are filled with water. It is especially fatal to small diameter pipes and pipes connected to measuring instruments. The purpose of this study is find operation and outdoor conditions where this phenomena can happen and also establish preventive measures to avoid this problem.
권오익(Oh Ik Kwon),김영일(Young Il Kim),김선혜(Sean Hay Kim) 대한설비공학회 2018 대한설비공학회 학술발표대회논문집 Vol.2018 No.11
The purpose of this study is to compare the change of external surface convection heat transfer coefficient based on wind speed change by building height. At a height of 10 m, wind speed is 3.5 m/s and external surface heat Transfer Coefficient is 22.59 W/(㎡·K). At 100 m height, wind speed is 7.5 m/s and external surface heat Transfer coefficient is 38.52 W/(㎡·K). At a height of 500 m, the wind speed is 12.7 m/s and the external surface heat Transfer coefficient is 59.50 W/(㎡·K). This difference in external surface heat transfer Coefficient does not change the building energy efficiency rating. Considering the long life cycle of the building, it is necessary to improve the insulation performance according to the height of the building.
권오익(Oh Ik Kwon),김영일(Young Il Kim),김선혜(Sean Hay Kim) 대한설비공학회 2021 설비공학 논문집 Vol.33 No.3
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.
김남익(Nam Ik Kim),김영일(Young Il Kim),최건식(Kun Sik Choi),성기홍(Ki Hong Seong) 한국유산소운동과학회 1999 한국유산소운동과학회지 Vol.3 No.1
The purpose of study was to investigate the influence of isokinetic knee joint muscle strength on duty form in the fire fighters. 28 men who aged 30-40 years were measured by isokinetic knee joint muscle strength testing(Cybex 6000 dynamometer system). Subjects were divided into the 2 groups that were indoor duty group and outdoor duty group. Statistical analysis was performed using analysis of variance t-test. In results, peak torque and average power of indoor duty group was showed significantly lower than that of outdoor duty group. At comparison of right and left knee joint, isokinetic strength, isokinetic power and isokinetic endurance of right knee joint was showed significantly higher then that of left knee joint, but in 60 degree per second, left knee joint was showed higher then that of right knee joint. Result from this investigation show greater presonal training and fire quell a riot, emergency rescue activity was increased knee joint muscle strength. Therefore, physical fitness and health maintance programs for fire fighters have been developed, implemented, and proven to reduce morbidity and mortality and financial expenditures.