A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings
Buildings account for a significant portion of our overall energy usage and associated greenhouse gas emissions. With the increasing concerns regarding climate change, there are growing needs for energy reduction and increasing our energy efficiency. Forecasting energy use plays a fundamental role i...
Main Authors: | Jason Runge, Radu Zmeureanu |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-01-01
|
Series: | Energies |
Subjects: | |
Online Access: | https://www.mdpi.com/1996-1073/14/3/608 |
Similar Items
-
Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review
by: Jason Runge, et al.
Published: (2019-08-01) -
A Deep-Learning-Based Meta-Modeling Workflow for Thermal Load Forecasting in Buildings: Method and a Case Study
by: Yuhao Zhou, et al.
Published: (2022-02-01) -
A Review of Energy Consumption Forecasting in Smart Buildings: Methods, Input Variables, Forecasting Horizon and Metrics
by: Deyslen Mariano-Hernández, et al.
Published: (2020-11-01) -
Clustering and Deep-Learning for Energy Consumption Forecast in Smart Buildings
by: Desiree Arias-Requejo, et al.
Published: (2023-01-01) -
BiGTA-Net: A Hybrid Deep Learning-Based Electrical Energy Forecasting Model for Building Energy Management Systems
by: Dayeong So, et al.
Published: (2023-09-01)