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In order to predict blended coal's property accurately, a new kind of hybrid prediction model based on principal component analysis (PCA) and weighted support vector machine (WSVM) was established. PCA was used to transform the high-dimensional and correlative influencing factors data to low-dimensional principal component subspace. These new features are(More)
Traditional time series forecasting models are difficult to capture the nonlinear patterns. Support vector regression (SVR) is a powerful tool for modeling the inputs and output(s) of complex and nonlinear systems. However, parameters determination for a SVR model is competent to the forecasting accuracy. Several evolutionary algorithms, such as genetic(More)
Accurate air-conditioning load forecasting is the precondition for the optimal control and energy saving operation of HVAC systems. Many forecasting techniques such as support vector machine (SVM), artificial neural network (ANN), autoregressive integrated moving average (ARIMA) and grey model, have been proposed in the field of air-conditioning load(More)
Accurate building cooling load forecasting is the precondition for the optimal control and energy saving operation of HVAC systems. Many forecasting approaches such as artificial neural network (ANN), support vector machine (SVM), autoregressive integrated moving average (ARIMA) and grey model, have been proposed in the field of building cooling load(More)
A number of different forecasting methods have been proposed for cooling load forecasting including historic method, real-time method, time series analysis, and artificial neural networks (ANN), but accuracy and time efficiency in prediction are a couple of contradictions to be hard to resolve for real-time traffic information prediction. In order to(More)
In this paper, a novel building cooling load forecasting approach combining kernel principal component analysis (KPCA) and support vector machine (SVM) is proposed. KPCA is an improved PCA, which possesses the property of extracting optimal features by adopting a nonlinear kernel function method. The original inputs are firstly transformed into nonlinear(More)
In this paper, a novel HVAC fan machinery fault recognition method combining kernel principal component analysis (KPCA) and support vector machine (SVM) is proposed. KPCA is an improved PCA, which possesses the property of extracting optimal features by adopting a nonlinear kernel function method. Support vector machine (SVM) is a novel approach based on(More)
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(More)
The varied-section tube heat exchanger (VSTHE) that enhances the shell-side and tube-side heat transfer simultaneously provides high heat transfer performance, cuts tube vibration failures and saves material since it does not require tube-support elements. Experiments were conducted on the baffle shell-and-tube heat exchanger (STHE) and VSTHE under(More)
Forecasting agriculture water demand is significant to optimize confirmation of water resources. In this study, we introduce a hybrid model which combines rough set theory and least square support vector machine to forecast the agriculture irrigation water demand. Through a certain district agriculture irrigation water demand dataset experiment, we have(More)