Modeling and forecasting building energy consumption: A review of data-driven techniques
Building energy consumption modeling and forecasting is essential to address buildings
energy efficiency problems and take up current challenges of human comfort, urbanization …
energy efficiency problems and take up current challenges of human comfort, urbanization …
Machine learning applications in urban building energy performance forecasting: A systematic review
In developed countries, buildings are involved in almost 50% of total energy use and 30% of
global green-house gas emissions. Buildings' operational energy is highly dependent on …
global green-house gas emissions. Buildings' operational energy is highly dependent on …
Comparison of Support Vector Machine and Extreme Gradient Boosting for predicting daily global solar radiation using temperature and precipitation in humid …
The knowledge of global solar radiation (H) is a prerequisite for the use of renewable solar
energy, but H measurements are always not available due to high costs and technical …
energy, but H measurements are always not available due to high costs and technical …
[HTML][HTML] Building performance simulation in the brave new world of artificial intelligence and digital twins: A systematic review
P de Wilde - Energy and Buildings, 2023 - Elsevier
In an increasingly digital world, there are fast-paced developments in fields such as Artificial
Intelligence, Machine Learning, Data Mining, Digital Twins, Cyber-Physical Systems and the …
Intelligence, Machine Learning, Data Mining, Digital Twins, Cyber-Physical Systems and the …
An investigation of feature selection methods for soil liquefaction prediction based on tree-based ensemble algorithms using AdaBoost, gradient boosting, and …
Previous major earthquake events have revealed that soils susceptible to liquefaction are
one of the factors causing significant damages to the structures. Therefore, accurate …
one of the factors causing significant damages to the structures. Therefore, accurate …
Estimation of SPEI meteorological drought using machine learning algorithms
Accurate estimation of drought events is vital for the mitigation of their adverse
consequences on water resources, agriculture and ecosystems. Machine learning …
consequences on water resources, agriculture and ecosystems. Machine learning …
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 …
smart cities. Recently, the novel data science and data-driven technologies have shown …
Machine learning modelling for predicting non-domestic buildings energy performance: A model to support deep energy retrofit decision-making
Non-domestic buildings contribute 20% of the UK's annual carbon emissions. A contribution
exacerbated by its ageing stock of which only 7% is considered new-build. Consequently …
exacerbated by its ageing stock of which only 7% is considered new-build. Consequently …
Urban energy use modeling methods and tools: A review and an outlook
N Abbasabadi, M Ashayeri - Building and environment, 2019 - Elsevier
Urban energy use modeling is important for understanding and managing energy
performance in cities. However, the existing methods and tools have limitations in …
performance in cities. However, the existing methods and tools have limitations in …
Buildings' energy consumption prediction models based on buildings' characteristics: Research trends, taxonomy, and performance measures
Building's energy consumption prediction is essential to achieve energy efficiency and
sustain-ability. Building's energy consumption is highly dependent on buildings' …
sustain-ability. Building's energy consumption is highly dependent on buildings' …