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À¯ÀüÇÁ·Î±×·¡¹Ö ±â°èÇнÀÀ» ÀÌ¿ëÇÑ ³Ãµ¿±â ½Ç½Ã°£ ÃÖÀûÁ¦¾î / Real-time Optimal Control of Chiller using Genetic Programming / Ãß°è-05. °ÇÃàȯ°æ¹×¼³ºñ |
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¼¿øÁØ(Suh, Won-Jun) ; ÃßÇѰæ(Chu, Han-Gyeong) ; ½ÅÇѼÖ(Shin, Han-Sol) ; ¶ó¼±Áß(Ra, Seon-Jung) ; ¹Úö¼ö(Park, Cheol-Soo) |
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´ëÇѰÇÃàÇÐȸ Çмú¹ßÇ¥´ëȸ ³í¹®Áý, Vol.36 No.2 (2016-10) |
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½ÃÀÛÆäÀÌÁö(627) ÃÑÆäÀÌÁö(2) |
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±â°èÇнÀ ; À¯ÀüÇÁ·Î±×·¡¹Ö ; ³Ãµ¿±â ; ÃÖÀû Á¦¾î ; Machine learning ; Genetic Programming ; Chiller ; Optimal Control |
| ¿ä¾à2 |
Recently, BEMS(Building Energy Management System) has been widely used in large buildings and there is a growing interest in model-assisted optimal control based on the BEMS data. Unfortunately, current BEMS has been used only for measurement, data collection and operation. It would be ideal that building energy model can be automatically made based on BEMS data and used for real-time optimal control for buildings. This paper presents such approach that the data-driven genetic programming can be beneficially utilized for development of a model of, and optimal control of a chiller. It is elaborated in the paper how the model of the chiller's electricity consumption was developed and validated using the genetic programming and actual BEMS data. |