Prediction Heating and Cooling Loads of Building Using Evolutionary Grey Wolf Algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Jitkongchuen:2019:DAMT-NCON,

• author = "Duangjai Jitkongchuen and Eakasit Pacharawongsakda",
• booktitle = "2019 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT-NCON)",
• title = "Prediction Heating and Cooling Loads of Building Using Evolutionary Grey Wolf Algorithms",
• year = "2019",
• pages = "93--97",
• abstract = "This paper proposes using evolutionary grey wolf algorithm to predict the heating load (HL) and the cooling load (CL) of buildings. The proposed algorithm was constructed using 768 various residential buildings with eight input variables (relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, glazing area distribution) and two output variables. The experimental results are evaluated by comparative to previous work, geometric semantic genetic programming (GSGP), artificial neural network (ANN), support vector regression (SVR), evolutionary multivariate adaptive regression splines (EMARS), random forests (RF) and multilayer perceptron (MLP). The results prove that the proposed algorithm is competitive compared to the other machine learning algorithms.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/ECTI-NCON.2019.8692232",
• month = jan,
• notes = "Also known as \cite{8692232}",

}

Genetic Programming entries for Duangjai Jitkongchuen Eakasit Pacharawongsakda

Citations