Applying support vector machines to predict building energy consumption in tropical region

By B. Dong, Chengpu Cao, S. Lee. 2005

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Comparative study of monthly inflow prediction methods for the Three Gorges Reservoir (2014, methodology): “2004), the stepwise search (Dong et al. 2005), SCE-UA algorithm (Lin et al.”
Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools (2012, background): “Various machine learning techniques such as polynomial regression [11], support vector machines (SVM) [10], [12] artificial neural networks (ANN) [13], [14], and decision trees [2] have been explored to predict various quantities of interest in the context of EPB.”
Time Series Prediction Using Support Vector Machines: A Survey (2009, methodology): “[63] discussed the use of SVM to predict “land-”
Using support vector machines for long-term discharge prediction (2006, methodology): “More recently, several other methods have been developed to identify the parameters, such as the stepwise search (Dong et al., 2005), genetic algorithms (Chen et al., 2004), the shuffled complex evolution algorithm (Yu et al., 2004), and the simulated annealing algorithms (Pai & Hong, 2005).”