Development and validation of regression models to predict monthly heating demand for residential buildings

By T. Catalina, Joseph Virgone, E. Blanco. 2008

365 citations recorded by the source corpus.

Subfields: Building and Construction, Electrical and Electronic Engineering, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/00b884d688b484a83cb0f517af150717437b116f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

1. Athanasios Tsanas
2. Angeliki Xifara

In Electrical and Electronic Engineering

1. Athanasios Tsanas
2. Angeliki Xifara

In Environmental Engineering

1. Athanasios Tsanas
2. Angeliki Xifara

Citations in the corpus

• Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools (2012, methodology): “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.”