摘要
精密空调集中系统一般是以最大负荷状况运行,但实际送风末端的负荷需求并不能够达到最大值,这造成了极大的能源浪费。为此,研究利用支持向量回归机对末端负荷需求进行合理预测,构建了空调负荷预测模型。然后根据将所得到的负荷预测值输入研究所设计的寻优模型中。研究利用天牛须搜索算法和粒子群算法构建了一种寻优模型,对集中空调进行智能控制。在研究过程中发现,该算法仍存在易陷于局部最优解,收敛精度低等局限性,为此引入自适应非线性惯性权重系数和Levy飞行策略对其进行优化,最终得到了一种新的集中空调智能控制系统。通过实验分析可知,模型最终产生总能耗为270.76 kW,平均节能率为27.55%,能够有效优化控制空调系统,实现节能减耗。
Precision air conditioning centralized systems generally operate at maximum load conditions,but the actual load demand at the supply end cannot reach the maximum value,resulting in significant energy waste.For this purpose,support vector regression machine was used to reasonably predict the end load demand and an air conditioning load prediction model was constructed.Then input the obtained load prediction values into the optimization model designed by the research institute.A search model was constructed using the Tenebrio search algorithm and particle swarm optimization algorithm to intelligently control centralized air conditioning.During the research process,it was found that the algorithm still has limitations such as being prone to falling into local optima and low convergence accuracy.Therefore,adaptive nonlinear inertia weight coefficients and Levy flight strategy were introduced to optimize it,and a new centralized air conditioning intelligent control system was ultimately obtained.Through experimental analysis,it can be concluded that the total energy consumption generated by the model is 270.76kW,with an average energy saving rate of 27.55%.This can effectively optimize the control of the air conditioning system and achieve energy conservation and consumption reduction.
作者
邓卜侨
谢岫峰
纪明阳
艾青
王康
冯光磊
DENG Buqiao;XIE Xiufeng;JI Mingyang;AI Qing;WANG Kang;FENG Guanglei(Beijing Fibrlink Communications Co.,Ltd.,BeiJing 100070,China)
出处
《自动化与仪器仪表》
2024年第2期149-153,共5页
Automation & Instrumentation
基金
北京中电飞华通讯有限公司科技项目《数据中心智慧大脑一期(智慧运营监控平台)研发及应用》(52680021 N01H)。
关键词
精密空调
粒子群算法
天牛须搜索算法
节能减耗
precision air conditioning
particle swarm optimization algorithm
longhorn whisker search algorithm
energy conservation and consumption reduction
