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) -
An Analysis of Short-Term Load Forecasting on Residential Buildings Using Deep Learning Models
by: Suresh, Sreerag
Published: (2020) -
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) -
Household-Level Energy Forecasting in Smart Buildings Using a Novel Hybrid Deep Learning Model
by: Dabeeruddin Syed, et al.
Published: (2021-01-01) -
A review and taxonomy of wind and solar energy forecasting methods based on deep learning
by: Ghadah Alkhayat, et al.
Published: (2021-06-01)