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논문 | Forecasting Energy Consumption of Actual Air Handling Unit and Absorpt…

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작성자 김지헌 작성일20-09-24 13:21 조회119회 댓글0건

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Air conditioning in buildings accounts for 60% of total energy consumption. Therefore, accurate predictions of energy consumption are needed to properly manage the energy consumption of buildings. For this purpose, many studies have been conducted recently on the prediction of energy consumption of buildings using machine learning techniques. The energy consumption of the air handling unit (AHU) and absorption chiller in an actual building’s air conditioning system is predicted in this paper using prediction models that are based on artificial neural networks (ANNs). Using these ANN models, the energy usage of the AHU and chiller could be predicted by collecting a month's worth of driving data during the summer cooling period. After the forecast models had been verified, the AHU prediction model showed performance in the ranges of 13.27% to 15.25% and 19.42% to 19.53% for the training period and testing period, respectively, and the mean bias error (MBE) ranges were 4.03% to 4.97% and 3.48% to 4.39% for the training period and testing period, respectively. The chiller prediction model satisfied energy consumption forecast performance criteria presented by ASHRAE guideline14(Measurement of Energy and Demand Savings) with performance of 24.64% ~ 25.58% and 7.12% ~ 29.39% in the training period and testing period, respectively, and MBE ranges of 2.59% ~ 3.40% and 1.35% ~ 2.87% in the training period and testing period, respectively. When the training period and testing period were combined for the AHU data, the actual energy usage forecast showed a lower error rate range of 0.22% to 1.11% for the training period and 0.17% to 2.44% for the testing period. For the chiller data, the error rate range was 0.22% to 2.12% for the entire training period, but was somewhat higher at 11.67% to 15.18% for the testing period. The study found that even if the performance criteria were met, high accuracy results were not obtained, which was due to poor data set quality. Although the forecast model based on artificial neural network can achieve relatively high accuracy results with sufficient amounts of data, it is believed that it will require thorough verification of the data used, as well as improvements in the predictive model to avoid overfitting and underfitting, to achieve such good results.

 
[이 게시물은 BEMS님에 의해 2020-10-14 16:23:52 연구성과에서 이동 됨]

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