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.
Dimensional sentiment analysis approach, which represents affective states as continuous numerical values on multiple dimensions, such as valence-arousal (VA) space, allows for more fine-grained analysis than the traditional categorical approach. In recent years, it has been applied in applications such as antisocial behavior detection, mood analysis and product review ranking. In this approach, an...
This work studies how to apply support vector machines in order to forecast the energy consumption of buildings. Usually, support vector regression is implemented using the sequential minimal optimisation algorithm. In this work, an alternative version of that algorithm is used to reduce the execution time. Several experiments were carried out taking into account data measured during one year. The...
In this paper, we focus on the prediction method of building energy consumption time series. The building energy consumption data can be regarded as a time series, which is usually nonlinear and non-stationary. Traditional time series analysis model has lower prediction accuracy. Then the machine learning method, especially support vector regression algorithm always has better performance to deal...
When using support vector regression to predict building energy consumption, since the energy influence factors are quite abundant and complex, the features associated with the statistical model could be in large quantity. This paper focuses in feature selection for the purpose of reducing model complexity without sacrificing performance. The optimal features are selected by their feasibility of obtaining...
In this study, a hybrid model using multivariate adaptive regression splines (MARS) and SVR is proposed for sales forecasting of information technology (IT) products. Support vector regression (SVR) has become a promising alternative for forecasting due to its generalization capability in obtaining a unique solution. However, one of the key problems is that SVR can not identify which forecasting variables...
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...
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...
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.