Evaluation of tree-based ensemble learning algorithms for building energy performance estimation
Published 2017 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Evaluation of tree-based ensemble learning algorithms for building energy performance estimation
Authors
Keywords
-
Journal
Journal of Building Performance Simulation
Volume 11, Issue 3, Pages 322-332
Publisher
Informa UK Limited
Online
2017-07-28
DOI
10.1080/19401493.2017.1354919
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Traffic sign detection and recognition based on random forests
- (2016) Ayoub Ellahyani et al. APPLIED SOFT COMPUTING
- A novel surrogate model to support building energy labelling system: A new approach to assess cooling energy demand in commercial buildings
- (2016) A.P. Melo et al. ENERGY AND BUILDINGS
- Integrating building performance simulation in agent-based modeling using regression surrogate models: A novel human-in-the-loop energy modeling approach
- (2016) Sokratis Papadopoulos et al. ENERGY AND BUILDINGS
- Assessing the potential of random forest method for estimating solar radiation using air pollution index
- (2016) Huaiwei Sun et al. ENERGY CONVERSION AND MANAGEMENT
- Investigating the use of gradient boosting machine, random forest and their ensemble to predict skin flavonoid content from berry physical–mechanical characteristics in wine grapes
- (2015) Luca Brillante et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- Prediction of energy performance of residential buildings: A genetic programming approach
- (2015) Mauro Castelli et al. ENERGY AND BUILDINGS
- Gated ensemble learning method for demand-side electricity load forecasting
- (2015) Eric M. Burger et al. ENERGY AND BUILDINGS
- A gradient boosting method to improve travel time prediction
- (2015) Yanru Zhang et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Modeling heating and cooling loads by artificial intelligence for energy-efficient building design
- (2014) Jui-Sheng Chou et al. ENERGY AND BUILDINGS
- Multi-objective optimization for building retrofit: A model using genetic algorithm and artificial neural network and an application
- (2014) Ehsan Asadi et al. ENERGY AND BUILDINGS
- Comparative study of a building energy performance software (KEP-IYTE-ESS) and ANN-based building heat load estimation
- (2014) Cihan Turhan et al. ENERGY AND BUILDINGS
- Estimating the energy consumption and power demand of small power equipment in office buildings
- (2014) A.C. Menezes et al. ENERGY AND BUILDINGS
- Development of surrogate models using artificial neural network for building shell energy labelling
- (2014) A.P. Melo et al. ENERGY POLICY
- Machine learning for Big Data analytics in plants
- (2014) Chuang Ma et al. TRENDS IN PLANT SCIENCE
- State of the art in building modelling and energy performances prediction: A review
- (2013) Aurélie Foucquier et al. RENEWABLE & SUSTAINABLE ENERGY REVIEWS
- Collinearity: a review of methods to deal with it and a simulation study evaluating their performance
- (2012) Carsten F. Dormann et al. ECOGRAPHY
- Thermal design of air-conditioned building for tropical climate using admittance method and genetic algorithm
- (2012) M. Sahu et al. ENERGY AND BUILDINGS
- Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools
- (2012) Athanasios Tsanas et al. ENERGY AND BUILDINGS
- A review on the prediction of building energy consumption
- (2012) Hai-xiang Zhao et al. RENEWABLE & SUSTAINABLE ENERGY REVIEWS
- Genetic algorithms based optimization of artificial neural network architecture for buildings’ indoor discomfort and energy consumption prediction
- (2012) Florent Boithias et al. Building Simulation
- Design Ensemble Machine Learning Model for Breast Cancer Diagnosis
- (2011) Sheau-Ling Hsieh et al. JOURNAL OF MEDICAL SYSTEMS
- Genetic-algorithm based approach to optimize building envelope design for residential buildings
- (2010) Daniel Tuhus-Dubrow et al. BUILDING AND ENVIRONMENT
- A decision tree method for building energy demand modeling
- (2010) Zhun Yu et al. ENERGY AND BUILDINGS
- Applying support vector machine to predict hourly cooling load in the building
- (2009) Qiong Li et al. APPLIED ENERGY
- Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network
- (2009) Laurent Magnier et al. BUILDING AND ENVIRONMENT
- Development and validation of regression models to predict monthly heating demand for residential buildings
- (2008) Tiberiu Catalina et al. ENERGY AND BUILDINGS
- User behavior in whole building simulation
- (2008) P. Hoes et al. ENERGY AND BUILDINGS
- A working guide to boosted regression trees
- (2008) J. Elith et al. JOURNAL OF ANIMAL ECOLOGY
- Sustainable development and climate change initiatives
- (2007) J.S. Damtoft et al. CEMENT AND CONCRETE RESEARCH
- An applied artificial intelligence approach towards assessing building performance simulation tools
- (2007) Abraham Yezioro et al. ENERGY AND BUILDINGS
- Contrasting the capabilities of building energy performance simulation programs
- (2006) Drury B. Crawley et al. BUILDING AND ENVIRONMENT
Become a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get StartedAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started