Artificial intelligence-based strategies for sustainable energy planning and electricity demand estimation: A systematic review

J Adinkrah, F Kemausuor, ET Tchao… - … and Sustainable Energy …, 2025 - Elsevier
Access to electricity is a cornerstone for sustainable development and is pivotal to a
country's progress. The absence of electricity impedes development and elevates poverty …

A comprehensive review of critical analysis of biodegradable waste PCM for thermal energy storage systems using machine learning and deep learning to predict …

A Sharma, PK Singh, E Makki, J Giri, T Sathish - Heliyon, 2024 - cell.com
This article explores the use of phase change materials (PCMs) derived from waste, in
energy storage systems. It emphasizes the potential of these PCMs in addressing concerns …

Filling time-series gaps using image techniques: Multidimensional context autoencoder approach for building energy data imputation

C Fu, M Quintana, Z Nagy, C Miller - Applied Thermal Engineering, 2024 - Elsevier
Building energy prediction and management has become increasingly important in recent
decades, driven by the growth of Internet of Things (IoT) devices and the availability of more …

Hybrid intelligent deep learning model for solar radiation forecasting using optimal variational mode decomposition and evolutionary deep belief network-online …

T Peng, Y Li, ZZ Song, Y Fu, MS Nazir… - Journal of Building …, 2023 - Elsevier
Accurate prediction of solar radiation is of great significance to improve the utilization of
solar energy for photovoltaic power generation on the roofs of urban buildings. Therefore, a …

Thermal modeling for control applications of 60,000 homes in North America using smart thermostat data

C Vallianos, J Candanedo, A Athienitis - Energy and Buildings, 2024 - Elsevier
As smart thermostats become increasingly available in residential buildings, there is an
opportunity to use measured building data to calibrate models for community and district …

Interpretable domain-informed and domain-agnostic features for supervised and unsupervised learning on building energy demand data

A Canaydin, C Fu, A Balint, M Khalil, C Miller, H Kazmi - Applied Energy, 2024 - Elsevier
Energy demand from the built environment is among the most important contributors to
greenhouse gas emissions. One promising way to curtail these emissions is through …

Improving the out-of-sample generalization ability of data-driven chiller performance models using physics-guided neural network

F Guo, A Li, B Yue, Z **ao, F **ao, R Yan, A Li, Y Lv… - Applied Energy, 2024 - Elsevier
Modeling of the chiller performance is essential for the implementation of optimal energy-
efficient control strategies in a heating, ventilation, and air conditioning (HVAC) system …

Creating synthetic energy meter data using conditional diffusion and building metadata

C Fu, H Kazmi, M Quintana, C Miller - Energy and Buildings, 2024 - Elsevier
Advances in machine learning and increased computational power have driven progress in
energy-related research. However, limited access to private energy data from buildings …

[HTML][HTML] Integrating urban building energy modeling (UBEM) and urban-building environmental impact assessment (UB-EIA) for sustainable urban development: A …

Y Li, H Feng - Renewable and Sustainable Energy Reviews, 2025 - Elsevier
Rapid urbanization has increased energy demand and environmental impacts in urban
buildings, highlighting the need to understand building interactions and energy transfer. This …

A critical perspective on current research trends in building operation: Pressing challenges and promising opportunities

E Saloux, K Zhang, JA Candanedo - Buildings, 2023 - mdpi.com
Despite the development of increasingly efficient technologies and the ever-growing amount
of available data from Building Automation Systems (BAS) and connected devices, buildings …