Machine Learning, Deep Learning and Statistical Analysis for forecasting building energy consumption—A systematic review

M Khalil, AS McGough, Z Pourmirza… - … Applications of Artificial …, 2022 - Elsevier
The building sector accounts for 36% of the total global energy usage and 40% of
associated Carbon Dioxide emissions. Therefore, the forecasting of building energy …

Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities

T Ahmad, D Zhang, C Huang, H Zhang, N Dai… - Journal of Cleaner …, 2021 - Elsevier
The energy industry is at a crossroads. Digital technological developments have the
potential to change our energy supply, trade, and consumption dramatically. The new …

[HTML][HTML] Energy modelling and control of building heating and cooling systems with data-driven and hybrid models—A review

Y Balali, A Chong, A Busch, S O'Keefe - Renewable and Sustainable …, 2023 - Elsevier
Implementing an efficient control strategy for heating, ventilation, and air conditioning
(HVAC) systems can lead to improvements in both energy efficiency and thermal …

Energy 4.0: AI-enabled digital transformation for sustainable power networks

M Khalid - Computers & Industrial Engineering, 2024 - Elsevier
The considerable developments in green technology have changed several industries,
including the energy business and power sector. These advances have transformed power …

A deep learning framework for building energy consumption forecast

N Somu, GR MR, K Ramamritham - Renewable and Sustainable Energy …, 2021 - Elsevier
Increasing global building energy demand, with the related economic and environmental
impact, upsurges the need for the design of reliable energy demand forecast models. This …

Modeling and forecasting building energy consumption: A review of data-driven techniques

M Bourdeau, X qiang Zhai, E Nefzaoui, X Guo… - Sustainable Cities and …, 2019 - Elsevier
Building energy consumption modeling and forecasting is essential to address buildings
energy efficiency problems and take up current challenges of human comfort, urbanization …

AI-empowered methods for smart energy consumption: A review of load forecasting, anomaly detection and demand response

X Wang, H Wang, B Bhandari, L Cheng - International Journal of Precision …, 2024 - Springer
This comprehensive review paper aims to provide an in-depth analysis of the most recent
developments in the applications of artificial intelligence (AI) techniques, with an emphasis …

Machine learning applications in urban building energy performance forecasting: A systematic review

S Fathi, R Srinivasan, A Fenner, S Fathi - Renewable and Sustainable …, 2020 - Elsevier
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 …

Prediction and optimization of energy consumption in an office building using artificial neural network and a genetic algorithm

M Ilbeigi, M Ghomeishi, A Dehghanbanadaki - Sustainable Cities and …, 2020 - Elsevier
The aim of this study is to propose a reliable method to optimize the energy consumption of
buildings. Also, the most effective input parameters are defined which are used in the energy …

[HTML][HTML] A review of data mining technologies in building energy systems: Load prediction, pattern identification, fault detection and diagnosis

Y Zhao, C Zhang, Y Zhang, Z Wang, J Li - Energy and Built Environment, 2020 - Elsevier
With the advent of the era of big data, buildings have become not only energy-intensive but
also data-intensive. Data mining technologies have been widely utilized to release the …