Building energy consumption forecast using multi-objective genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8120
- @Article{Tahmassebi:2018:Measurement,
-
author = "Amirhessam Tahmassebi and Amir H. Gandomi",
-
title = "Building energy consumption forecast using
multi-objective genetic programming",
-
journal = "Measurement",
-
year = "2018",
-
volume = "118",
-
pages = "164--171",
-
keywords = "genetic algorithms, genetic programming, Energy
performance, Symbolic regression",
-
ISSN = "0263-2241",
-
URL = "https://www.sciencedirect.com/science/article/pii/S0263224118300447",
-
DOI = "doi:10.1016/j.measurement.2018.01.032",
-
abstract = "A multi-objective genetic programming (MOGP) technique
with multiple genes is proposed to formulate the energy
performance of residential buildings. Here, it is
assumed that loads have linear relation in terms of
genes. On this basis, an equation is developed by MOGP
method to predict both heating and cooling loads. The
proposed evolutionary approach optimizes the most
significant predictor input variables in the model for
both accuracy and complexity, while simultaneously
solving the unknown parameters of the model. In the
proposed energy performance model, relative compactness
has the most and orientation the least contribution.
The proposed MOGP model is simple and has a high degree
of accuracy. The results show that MOGP is a suitable
tool to generate solid models for complex nonlinear
systems with capability of solving big data problems
via parallel algorithms.",
- }
Genetic Programming entries for
Amirhessam Tahmassebi
A H Gandomi
Citations