Building energy consumption forecast using multi-objective genetic programming

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

@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