基于改进FCM算法的校园空调冷负荷错峰研究

The Study of Campus Cooling Peak Load Shifting based on Improved FCM Algorithm

校园能耗占社会能耗比重不断上升,为满足舒适的教学科研环境,尤其夏季校区空调耗电量占比较高。由于校区各类建筑具有不同的使用时间,可以通过错峰管理来降低空调耗电量,保持电网稳定。为研究负荷错峰潜力,本篇利用最近最远得分指标(NFS)改进模糊C均值聚类算法(Fuzzy C--means),结合相关性分析等数据分析方法,利用校园中央空调系统监管平台分项计量数据进行空调负荷时序规律和空间分布特征的统计分析,结果表明校园负荷可以聚类为以办公教学,宿舍休息和过渡时间为主的三类,需要加强管理来开展错峰调节。本篇延伸了分项计量数据的后续应用,为优化校园空调系统运行管理提供方法和借鉴。

Along with the development of higher education, campus energy cost increase a lot. To fulfill the demand of comfortable environment, the cost of air condition system makes up a large proportion in campus power consumption.Because of the different schedule of buildings, the peak shifting can be used to decrease the energy cost and maintain the stability of power system. To study the potential of cooling load peak shifting, this article applied the nearest and furthest score to improve the Fuzzy C-means algorithm by automatically decide the number of the cluster, then, combining with other data analyze method like correlation analysis, this article dose statistical analysis based on the sub-metering data of air conditioning monitoring platform. The result show that the cooling load demand could be divided into three clusters,the working hour, the rest hour and the transition hour, needing to improve the management to fulfill peak shifting. The statistical data are studied for the sustainable use, which provides the basis for the operation and management of buildings.