Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption

By A. Neto, F. Fiorelli. 2008

656 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Building and Construction, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/7fa3a40a05d4cc76932f04b5e27d3a6a6e502f90

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. Marcus M. Keane
2. Paul Raftery
3. Daniel Coakley

In Building and Construction

1. Marcus M. Keane
2. Paul Raftery
3. Daniel Coakley

In Electrical and Electronic Engineering

1. Marcus M. Keane
2. K. Rajurkar
3. Paul Raftery
4. Daniel Coakley

Citations in the corpus

• A review of methods to match building energy simulation models to measured data (2014, methodology): “[76 – 79] PSTAR Primary and secondary term analysis and re-normalisation Analytical tool for the meaningful estimation of parametrs of a complex building from a few data channels over a short period (a few days).”
• 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].”