Artificial neural network structure optimisation for accurately prediction of exergy, comfort and life cycle cost performance of a low energy building-Reference-Cited by-同舟云学术

Artificial neural network structure optimisation for accurately prediction of exergy, comfort and life cycle cost performance of a low energy building

Published:2020-12 Issue: Volume:280 Page:115862
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

García Kerdan Iván,Morillón Gálvez David

Funder

Coordinación de la Investigación Científica

Universidad Nacional Autónoma de México

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Mechanical Engineering,General Energy,Building and Construction

Reference67 articles.

1. A review on buildings energy consumption information;Pérez-Lombard;Energy Build,2008

2. IPCC. Fifth assessment report of the intergovernmental panel on climate change. In: Press CU, editor. Cambridge, United Kingdom and New York, NY, USA; 2014.

3. IEA. Energy eficiency 2019. In: Agency IE, editor. Paris, France; 2019.

4. Assessing gaps and needs for integrating building performance optimization tools in net zero energy buildings design;Attia;Energy Build,2013

5. A review on simulation-based optimization methods applied to building performance analysis;Nguyen;Appl Energy,2014

Cited by 38 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A novel multi-objective generative design approach for sustainable building using multi-task learning (ANN) integration;Applied Energy;2024-12

2. A Review of Research on Building Energy Consumption Prediction Models Based on Artificial Neural Networks;Sustainability;2024-09-07

3. Surrogate modelling methodology for predicting annual site energy for single-family wartime bungalow archetypes in Toronto;Energy and Buildings;2024-05

4. Multi-Objective Optimization with Active–Passive Technology Synergy for Rural Residences in Northern China;Energies;2024-03-23

5. Artificial Neural Network–Based Generative Design Optimization of the Energy Performance of Florida Single-Family Houses;Journal of Computing in Civil Engineering;2024-03