[HTML][HTML] Interpretable machine learning for building energy management: A state-of-the-art review

Z Chen, F Xiao, F Guo, J Yan - Advances in Applied Energy, 2023 - Elsevier
Abstract Machine learning has been widely adopted for improving building energy efficiency
and flexibility in the past decade owing to the ever-increasing availability of massive building …

A Future Direction of Machine Learning for Building Energy Management: Interpretable Models

L Gugliermetti, F Cumo, S Agostinelli - Energies, 2024 - mdpi.com
Machine learning (ML) algorithms are now part of everyday life, as many technological
devices use these algorithms. The spectrum of uses is wide, but it is evident that ML …

Tackling Uncertainty: Forecasting the Energy Consumption and Demand of an Electric Arc Furnace with Limited Knowledge on Process Parameters

V Zawodnik, FC Schwaiger, C Sorger, T Kienberger - Energies, 2024 - mdpi.com
The iron and steel industry significantly contributes to global energy use and greenhouse
gas emissions. The rising deployment of volatile renewables and the resultant need for …

Optimization and management of microgrids in the built environment based on intelligent digital twins

S Agostinelli - 2024 - iris.uniroma1.it
The interplay between the built environment and energy use has profound implications for
global energy consumption, emissions, and the transition towards sustainable systems …

[PDF][PDF] Advances in Applied Energy

W Zheng, J Hu, Z Wang, J Li, Z Fu, H Li, J Jurasz… - researchgate.net
abstract Heating, ventilation and air-conditioning (HVAC) system is favourable for regulating
indoor temperature, relative humidity, airflow pattern and air quality. However, HVAC …