Systematic review of deep learning and machine learning for building energy

S Ardabili, L Abdolalizadeh, C Mako, B Torok… - Frontiers in Energy …, 2022 - frontiersin.org
The building energy (BE) management plays an essential role in urban sustainability and
smart cities. Recently, the novel data science and data-driven technologies have shown …

Machine learning methods in smart lighting toward achieving user comfort: a survey

AG Putrada, M Abdurohman, D Perdana… - IEEE access, 2022 - ieeexplore.ieee.org
Smart lighting has become a universal smart product solution, with global revenues of up to
US 5.9 billion by 2021. Six main factors drive the technology: light-emitting diode (LED) …

A review on the current usage of machine learning tools for daylighting design and control

J Ngarambe, I Adilkhanova, B Uwiragiye… - Building and …, 2022 - Elsevier
Proper use of daylighting improves visual and thermal comfort in indoor environments and
minimizes dependency on artificial lighting, saving substantial amounts of energy …

Simulating annual autoregulation of daylight by grating smart window with angular-selective transmission

RS Zakirullin, IA Odenbakh - Journal of Building Performance …, 2024 - Taylor & Francis
As a basis for further development of the BPS computer software, a method for simulating
the characteristics of buildings with grating smart windows is proposed. This novel smart …

Optimizing classroom modularity and combinations to enhance daylighting performance and outdoor platform through ANN acceleration in the post-epidemic era

Y Liu, K Chen, E Ni, Q Deng - Heliyon, 2023 - cell.com
The global COVID-19 pandemic has increased attention to the relationship between the built
environment and health, particularly in educational settings where students spend a …

A review on behavioural propensity for building load and energy profile development–Model inadequacy and improved approach

A Ramokone, O Popoola, A Awelewa… - … Energy Technologies and …, 2021 - Elsevier
To achieve building energy conservation goals, models for forecasting energy consumption
with energy-related occupant behavior inclusive must be encompassed in energy …

Deep neural network approach for annual luminance simulations

Y Liu, A Colburn, M Inanici - Journal of Building Performance …, 2020 - Taylor & Francis
Annual luminance maps provide meaningful evaluations for occupants' visual comfort and
perception. This paper presents a novel data-driven approach for predicting annual …

Performance of neural network for indoor airflow prediction: Sensitivity towards weight initialization

Q Zhou, R Ooka - Energy and Buildings, 2021 - Elsevier
Neural networks (NNs) have been proposed as a promising alternative for fast and accurate
prediction of indoor airflow. NN training is of great importance for acquiring accurate …

A low-cost framework to establish internal blind control patterns and enable simulation-based user-centric design

MV Bavaresco, E Ghisi - Journal of Building Engineering, 2020 - Elsevier
The literature emphasises the important role that occupants play on the energy performance
of buildings, and increasing knowledge of drivers for human-building interactions is …

[HTML][HTML] Metamodeling of the Energy Consumption of Buildings with Daylight Harvesting–Application of Artificial Neural Networks Sensitive to Orientation

RW da Fonseca, FOR Pereira - Journal of Daylighting, 2021 - solarlits.com
Daylight harvesting is a well-known strategy to address building energy efficiency. However,
few simplified tools can evaluate its dual impact on lighting and air conditioning energy …