The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
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 prediction. However, none of them has enough accuracy...
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 algorithms and simulated annealing algorithms...
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. So the accuracy of forecasting is influenced by many unpredicted factors...
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 principal components using KPCA. These new...
This study develops a novel methodology hybridizing genetic algorithms (GAs) and support vector regression (SVR) and implements this model in a problem forecasting hourly cooling load. The aim of this study is to examine the feasibility of SVR in building cooling load forecasting by comparing it with back-propagation neural networks (BPNN) and the autoregressive integrated moving average (ARIMA) model...
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 improve time efficiency of prediction, a new hourly...
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 prediction. However, none of them has enough accuracy...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.