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题名: Multi-objective Optimal Control Algorithm for HVAC Based on Particle Swarm Optimization
作者: Zhang YY(张延宇) ; Ceng P(曾鹏) ; Zang CZ(臧传治)
作者部门: 工业控制网络与系统研究室
会议名称: 5th International Conference on Intelligent Control and Information Processing (ICICIP 2014)
会议日期: August 18-20, 2014
会议地点: Dalian, China
会议录: 5th International Conference on Intelligent Control and Information Processing (ICICIP 2014)
会议录出版者: IEEE
会议录出版地: Piscataway, NJ, USA
出版日期: 2014
页码: 417-423
摘要: Residential sector is the biggest potential field of reducing peak demand through demand response (DR) in smart grid. Heating, ventilating, and air conditioning (HVAC) is the largest residential electricity user in house. Therefore, controlling the operation of HVAC is an effective method to implement DR in residential sector. The algorithms proposed in literature are single objective optimization algorithms that only minimize the electricity cost and could not quantify the user’s comfort level. To tackle this problem, this paper proposes a comfort level indicator, builds a multi-objective scheduling model, and presents a multi-objective optimal control algorithm for HVAC based on particle swarm optimization (PSO). The algorithm controls the operation of HVAC according to electricity price, outdoor temperature forecast, and user preferences to minimize the electricity cost and maximize the user comfort level simultaneously. The proposed algorithm is verified by simulations, and the results demonstrate that it can decrease the electricity cost significantly and maintain the user comfort level effectively.
语种: 英语
产权排序: 1
内容类型: 会议论文
URI标识: http://ir.sia.cn/handle/173321/15364
Appears in Collections:工业控制网络与系统研究室_会议论文

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Recommended Citation:
张延宇;曾鹏;臧传治.Multi-objective Optimal Control Algorithm for HVAC Based on Particle Swarm Optimization.见:IEEE.5th International Conference on Intelligent Control and Information Processing (ICICIP 2014) ,Piscataway, NJ, USA,2014,417-423
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文件名: Multi-objective optimal control algorithm for air conditioning system based on pariticle swarm optimization.pdf
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