Building Cooling Load Forecasting Using Fuzzy Support Vector Machine and Fuzzy C-Mean Clustering
Accurate building cooling load forecasting is the precondition for the optimal control and energy saving operation of HVAC systems. Hourly cooling load forecasting is a difficult work as the load at a given point is dependent not only on the load at the previous hour but also on the load at the same hour on the previous day. In this paper, a novel shortterm cooling load forecasting approach is presented by conjunctive use of fuzzy C-mean clustering algorithm and fuzzy support vector machines (FSVMs). According to the similarity degree of input samples, the training samples are clustered by means of the homogenous characteristic, and then we apply a fuzzy membership to each input point such that different input points can make different contributions to the learning of decision surface. The results of experiment indicate that the proposed method can be used as an attractive and effective means for shortterm cooling load forecasting.
Building cooling prediction fuzzy support vector machine FCM
Li Xuemei Deng Yuyan Ding Lixing Jiang Liangzhong
Institute of Built Environment and Control, Zhongkai University of Agriculture and Engineering, Guan Institute of Built Environment and Control, Zhongkai University of Agriculture and Engineering, Guan School of Mechanical & Automotive Engineering, South China University of Technology, Guangzhou, Chin
国际会议
成都
英文
438-441
2010-06-12（万方平台首次上网日期，不代表论文的发表时间）