JOURNAL OF LIGHT INDUSTRY

CN 41-1437/TS  ISSN 2096-1553

Volume 31 Issue 3
May 2016
Article Contents
CAO Xiang-hong, WANG Peng-hui and ZHANG Hua. Split optimization research on public building energy consumption based on simulated annealing and particle swarm optimization algorithm[J]. Journal of Light Industry, 2016, 31(3): 81-88. doi: 10.3969/j.issn.2096-1553.2016.3.011
Citation: CAO Xiang-hong, WANG Peng-hui and ZHANG Hua. Split optimization research on public building energy consumption based on simulated annealing and particle swarm optimization algorithm[J]. Journal of Light Industry, 2016, 31(3): 81-88. doi: 10.3969/j.issn.2096-1553.2016.3.011 shu

Split optimization research on public building energy consumption based on simulated annealing and particle swarm optimization algorithm

  • Received Date: 2015-09-24
    Available Online: 2016-05-15
  • For public building sub metering in energy resolution of problems, the improved SA_PSO algorithm was put forward. The algorithm simulated the combination of annealing mechanism and particle swarm optimization algorithm, introduced particle velocity shrinkage factor and the optimal particle roulette gambling strategies for perturbation. Using SA_PSO algorithm to optimize the public building energy resolution mathematical model parameters, the hourly energy consumption data from split could be gotten. The simulation results showed that SA_PSO algorithm had better convergence performance, could effectively avoid the particles falling into local minimum energy consumption parameters correction value and quickly reached the global optimum.
  • 加载中
    1. [1]

      李百战,张宇,丁勇,等.重庆市公共建筑能源管理现状分析[J].暖通空调,2010,40(9):116.

    2. [2]

      HARVEY L D D,KORYTAROVA K,LUCON O,et al.Volha Roshchanka Construction of a global disaggregated dataset of building energy use and floor area in 2010[J].Energy and buildings,2014,76:488.

    3. [3]

      YANG C F,LI H J,REZGUI Y,et al.High throughput computing based distributed genetic algorithm for building energy consumption optimization[J].Energy and Buildings,2014,76:92.

    4. [4]

      GUO H.Accelerated continuous conditional random fields for load forecasting[J].IEEE transactions on knowledge and data engineering,2015,27(8):2023.

    5. [5]

      LU T H,HUANG Z J,ZHANG T.Method and case study of quantitative uncertainty analysis in building energy consumption inventories[J].Energy and buildings,2013,57:193.

    6. [6]

      李俊.基于分项计量系统的建筑能耗拆分与节能潜力分析研究[D].重庆:重庆大学,2008.

    7. [7]

      王远.大型公共建筑用电分项计量方法研究[D].北京:清华大学,2008.

    8. [8]

      MALYS L,MUSY M,INARD C.A hydrothermal model to assess the impact of green walls on urban microclimate and building energy consumption[J].Building and environment,2014,73:187.

    9. [9]

      都国兵.基于遗传算法的变权重组合预测模型研究及应用[D].兰州:兰州大学,2011.

    10. [10]

      杨晓燕,林琳.一种基于粗糙集和粒子群优化算法的权重确定方法[J].闽江学院学报,2010,31(5):74.

    11. [11]

      董鹏.基于支持向量机的舰船建造费组合预测方法研究[J].造船技术,2011(1):13.

    12. [12]

      耿建军.基于GRNN神经网络的变组合预测的权重确定方法[J].教学的实践与认识,2011,41(3):86.

    13. [13]

      牛祺飞,张永坚,张春华.建筑中能耗拆分方法[J].控制工程,2010,17(1):81.

    14. [14]

      TROVAO J P F,SANTOS V D N,PEREIRINHA P G,et al.A simulated annealing approach for optimal power source management in a small EV[J].IEEE transactions on sustainable energy,2013,4(4):873.

    15. [15]

      杨洁,蒋林,赛清平,等.基于模拟退火粒子群优化的光伏多峰最大功率跟踪算法[J].计算机应用,2014,34(S1):330.

    16. [16]

      焦晓璇,景博,黄以峰,等.基于模拟退火离散粒子群算法的测试点优化[J].计算机应用,2014,34(6):1649.

    17. [17]

      龚纯,王正林.精通Matlab最优计算[M].北京:电子工业出版社,2012.

    18. [18]

      中国建筑标准设计研究院.建筑电气常用数据:04DX101-1[S].北京:中国计划出版社,2006.

    19. [19]

      袁代林.粒子群优化算法的变形[J].计算机工程与应用,2015,5(15):23.

    20. [20]

      羌晓清,景博,邓森,等.基于模拟退火粒子群算法的不可靠测试点优化[J].计算机应用,2015,35(4):1071.

    21. [21]

      林娟,杜庆良,杨辉,等.基于粒子群优化算法的并行模拟退火算法[J].计算机科学与探索,2014,8(7):887.

Article Metrics

Article views(977) PDF downloads(51) Cited by()

Ralated
    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return