Regression tree ensemble learning-based prediction of the heating and cooling loads of residential buildings
- 23 May 2022
- journal article
- research article
- Published by Springer Science and Business Media LLC in Building Simulation
- Vol. 15 (11), 2003-2017
- https://doi.org/10.1007/s12273-022-0908-x
Abstract
No abstract availableKeywords
This publication has 45 references indexed in Scilit:
- Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning toolsEnergy and Buildings, 2012
- Forecasting building energy consumption using neural networks and hybrid neuro-fuzzy system: A comparative studyEnergy and Buildings, 2011
- A decision tree method for building energy demand modelingEnergy and Buildings, 2010
- Prediction of energy demands using neural network with model identification by global optimizationEnergy Conversion and Management, 2009
- Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networksEnergy Conversion and Management, 2009
- Development and validation of regression models to predict monthly heating demand for residential buildingsEnergy and Buildings, 2008
- Boosting Algorithms: Regularization, Prediction and Model FittingStatistical Science, 2007
- Applying support vector machines to predict building energy consumption in tropical regionEnergy and Buildings, 2005
- Hourly cooling load prediction by a combined forecasting model based on Analytic Hierarchy ProcessInternational Journal of Thermal Sciences, 2004
- EnergyPlus: creating a new-generation building energy simulation programEnergy and Buildings, 2001