Applying support vector machine to predict hourly cooling load in the building

By Qiong Li, Q. Meng, Jiejin Cai, H. Yoshino, A. Mochida. 2009

485 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Building and Construction, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/b180d17e4c30d55114de4167c94e93d24a4636b2

How researchers see this paper

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

In Artificial Intelligence

1. Angeliki Xifara
2. Athanasios Tsanas

In Building and Construction

1. Athanasios Tsanas
2. Angeliki Xifara

In Computer Vision and Pattern Recognition

1. Athanasios Tsanas
2. Angeliki Xifara

Citations in the corpus

• Hourly cooling load forecasting using time-indexed ARX models with two-stage weighted least squares regression (2014, background): “Artificial neural networks (ANN) and support vector machines (SVM) are also common models for cooling load prediction [15] [16] [17] [18] [19] [20] [21] [22] [23] [24].”
• Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools (2012, background): “[12] forecast hourly building CL based mainly on preceding environmental parameters.”