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Cooling Load Prediction Based on the Combination of Rough Set Theory and Support Vector Machine
Load forecasting is key for the optimal control of heating, ventilating, and air-conditioning. However, accurate forecasting modeling requires the ability of selecting relevant factors so that the influences of the irrelevant factors can be reduced substantially. This paper presents a novel hybrid method combining rough set theory and support vector machine network (RSSV). Rough set (RS) theory is applied to find the relevant factors of the load, which are used as the inputs of support vector machine (SVM) to predict cooling load. To improve the accuracy and enhance the robustness of load forecasting results, a general load prediction model synthesizing multi-RSSV (MRSSV) is presented so as to make full use of redundant information. Optimum principle is employed to deduce the weights of each RSSV model. Actual prediction results for a real HVAC system show that the MRSSV forecasting model, whose relative error turned out to be within 4%, may be better than those models using individual RSSV, recursive least squares (RLS), and autoregressive integrated moving average (ARIMA). In addition, individual RSSV forecasting results are also better than that of ARIMA.
Cooling Load Prediction Based on the Combination of Rough Set Theory and Support Vector Machine
Load forecasting is key for the optimal control of heating, ventilating, and air-conditioning. However, accurate forecasting modeling requires the ability of selecting relevant factors so that the influences of the irrelevant factors can be reduced substantially. This paper presents a novel hybrid method combining rough set theory and support vector machine network (RSSV). Rough set (RS) theory is applied to find the relevant factors of the load, which are used as the inputs of support vector machine (SVM) to predict cooling load. To improve the accuracy and enhance the robustness of load forecasting results, a general load prediction model synthesizing multi-RSSV (MRSSV) is presented so as to make full use of redundant information. Optimum principle is employed to deduce the weights of each RSSV model. Actual prediction results for a real HVAC system show that the MRSSV forecasting model, whose relative error turned out to be within 4%, may be better than those models using individual RSSV, recursive least squares (RLS), and autoregressive integrated moving average (ARIMA). In addition, individual RSSV forecasting results are also better than that of ARIMA.
Cooling Load Prediction Based on the Combination of Rough Set Theory and Support Vector Machine
Hou, Zhijian (author) / Lian, Zhiwei (author) / Yao, Ye (author) / Yuan, Xinjian (author)
HVAC&R Research ; 12 ; 337-352
2006-04-01
16 pages
Article (Journal)
Electronic Resource
Unknown
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