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±â°èÇнÀÀ» ÀÌ¿ëÇÑ ³Ãµ¿±â ÃÖÀû °¡µ¿/Á¤Áö ½ÃÁ¡ °áÁ¤ ¸ðµ¨ °³¹ß / Determination of optimal start/stop time of a chiller using machine learning / Ãß°è-01. ÀϹݺι® |
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¹Ú¼ºÈ£(Park, SungHo) ; ¾È±â¾ð(Ahn, Ki Uhn) ; Ȳ½ÂÈ£(Hwang, Aaron) ; ÃÖ¼±±Ô(Choi, Sunkyu) ; ¹Úö¼ö(Park, Cheol Soo) |
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´ëÇѰÇÃàÇÐȸ Çмú¹ßÇ¥´ëȸ ³í¹®Áý, Vol.38 No.2 (2018-10) |
ÆäÀÌÁö |
½ÃÀÛÆäÀÌÁö(297) ÃÑÆäÀÌÁö(2) |
ÁÖÁ¦¾î |
Àΰø½Å°æ¸Á ; ±â°èÇнÀ ; °Ç¹° ¿¡³ÊÁö °ü¸® ½Ã½ºÅÛ ; ¸ðµ¨ ±â¹Ý Á¦¾î ; µ¥ÀÌÅͱâ¹Ý ¸ðµ¨. ; Artificial Neural Network ; Machine Learning ; BEMS ; Model Predictive Control ; Data-driven Model |
¿ä¾à2 |
In this study, the authors developed ANN(Artificial Neural Network) models using Building Energy Management System (BEMS) data of a real-life office building. The ANN models are used to predict chilled water temperature leaving from a chiller system at the sampling time of 15 minutes for the time horizon of two hours. Based on the prediction of the ANN models, optimal control (optimal start and time) strategies of a chiller and Air Handling Unit (AHU) were developed. Due to ANN models, the operation time of a chiller can be saved by 4.7%. |