BUILDING SIMPLE MATHEMATICAL MODELS TO CALCULATE THE ENERGY REQUIREMENTS OF BUILDINGS.

被引:0
|
作者
Victoria Mercado, Maria [1 ]
Javier Barea-Paci, Gustavo [1 ]
Esteban Acena, Andres [2 ]
机构
[1] Consejo Nacl Invest Cient & Tecn, Inst Ambiente Habitat & Energia INAHE, Mendoza, Argentina
[2] Univ Nacl Cuyo, Fac Ciencias Exactas & Nat, Inst Interdisciplinario Ciencias Basicas, CONICET, Mendoza, Argentina
来源
REVISTA HABITAT SUSTENTABLE | 2023年 / 13卷 / 02期
关键词
mathematical modeling; simulations; sustainable architecture; PREDICTION; METHODOLOGY;
D O I
10.22320/07190700.2023.13.02.04
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This work looks to build a predictive mathematical model that can provide a first approach to a building's energy requirement (ER) value in a temperate continental climate. The aim is to contribute to the theoretical knowledge of energy assessment tools. To do this, parametric simulations were run and processed using the EnergyPlus 9.5 and JePlus programs. The results were then used as a dataset to build different mathematical models, using the SageMath program to run equations that predicted the ER of each scenario. Work was done with the models, scaling their complexity with the methods and the number of parameters used. Finally, a model with a low error (0.08) and 15 parameters was chosen. It was noted that, although increasing the number of parameters brought the models closer to a 0.02 error, there was a risk of overfitting. The chosen model seeks to incorporate dynamic simulations' accuracy and validity into a simple prediction tool that construction professionals can apply.
引用
收藏
页码:50 / 61
页数:12
相关论文
共 50 条
  • [31] Impacts of Building Microenvironment on Energy Consumption in Office Buildings: Empirical Evidence from the Government Office Buildings in Guangdong Province, China
    Li, Zhaoji
    Peng, Shihong
    Cai, Weiguang
    Cao, Shuangping
    Wang, Xia
    Li, Rui
    Ma, Xianrui
    BUILDINGS, 2023, 13 (02)
  • [32] Building intelligent alarm systems by combining mathematical models and inductive machine learning techniques
    Muller, B
    Hasman, A
    Blom, JA
    INTERNATIONAL JOURNAL OF BIO-MEDICAL COMPUTING, 1996, 41 (02): : 107 - 124
  • [33] A Simple Indoor Localization Methodology for Fast Building Classification Models Based on Fingerprints
    Sanchez-Rodriguez, David
    Alonso-Gonzalez, Itziar
    Ley-Bosch, Carlos
    Quintana-Suarez, Miguel A.
    ELECTRONICS, 2019, 8 (01)
  • [34] The Building Energy Performance Gap in Multifamily Buildings: A Detailed Case Study Analysis of the Energy Demand and Collective Heating System
    van de Putte, Stijn
    Steeman, Marijke
    Janssens, Arnold
    SUSTAINABILITY, 2025, 17 (01)
  • [35] Achieving nearly zero energy buildings in Cyprus, through building performance simulations, based on the use of innovative energy technologies
    Dracou, Marina Kyprianou
    Santamouris, Mat
    Papanicolas, Costas N.
    SUSTAINABILITY IN ENERGY AND BUILDINGS 2017, 2017, 134 : 636 - 644
  • [36] Calibrating whole building energy models: An evidence-based methodology
    Raftery, Paul
    Keane, Marcus
    O'Donnell, James
    ENERGY AND BUILDINGS, 2011, 43 (09) : 2356 - 2364
  • [37] Evaluation of "Autotune" calibration against manual calibration of building energy models
    Chaudhary, Gaurav
    New, Joshua
    Sanyal, Jibonananda
    Im, Piljae
    O'Neill, Zheng
    Garg, Vishal
    APPLIED ENERGY, 2016, 182 : 115 - 134
  • [38] A Future Direction of Machine Learning for Building Energy Management: Interpretable Models
    Gugliermetti, Luca
    Cumo, Fabrizio
    Agostinelli, Sofia
    ENERGIES, 2024, 17 (03)
  • [39] Evaluating different levels of information on the calibration of building energy simulation models
    Cheng, Siyu
    Tekler, Zeynep Duygu
    Jia, Hongyuan
    Li, Wenxin
    Chong, Adrian
    BUILDING SIMULATION, 2024, 17 (04) : 657 - 676
  • [40] Stochastic models for building energy prediction based on occupant behavior assessment
    Virote, Joao
    Neves-Silva, Rui
    ENERGY AND BUILDINGS, 2012, 53 : 183 - 193