Potential Analysis of the Attention-Based LSTM Model in Ultra-Short-Term Forecasting of Building HVAC Energy Consumption
Predicting system energy consumption accurately and adjusting dynamic operating parameters of the HVAC system in advance is the basis of realizing the model predictive control (MPC). In recent years, the LSTM network had made remarkable achievements in the field of load forecasting. This paper aimed...
Main Authors: | Yang Xu, Weijun Gao, Fanyue Qian, Yanxue Li |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2021-08-01
|
Series: | Frontiers in Energy Research |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2021.730640/full |
Similar Items
-
Accurate Deep Model for Electricity Consumption Forecasting Using Multi-Channel and Multi-Scale Feature Fusion CNN–LSTM
by: Xiaorui Shao, et al.
Published: (2020-04-01) -
SA-JSTN: Self-Attention Joint Spatiotemporal Network for Temperature Forecasting
by: Lukui Shi, et al.
Published: (2021-01-01) -
A Comparison of ARIMAX, VAR and LSTM on Multivariate Short-Term Traffic Volume Forecasting
by: Bhanuka Dissanayake, et al.
Published: (2021-01-01) -
Multi-Step Short-Term Power Consumption Forecasting Using Multi-Channel LSTM With Time Location Considering Customer Behavior
by: Xiaorui Shao, et al.
Published: (2020-01-01) -
Short‐term wind speed multistep combined forecasting model based on two‐stage decomposition and LSTM
by: Xuechao Liao, et al.
Published: (2021-09-01)