.png){kind=icon}
Return
A knowledge embedded graph neural network-based cooling load prediction method using dynamic data association
.png){kind=icon}
PRE
AI
DOI：10.1016/j.enbuild.2022.112635.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Accurate cooling load prediction has continually attracted interest due to its important role in building energy saving. Much attention to existing methods has been paid to the temporal information of the features that affect the load prediction. However, facing the time-varying interactions between various types of features, existing approaches have limited ability to extract the implicit information of the complex associations among them. This limitation leads to underutilization of features, especially those that indirectly affect the load prediction through such interactions, and further constrain prediction accuracy. To this end, this paper proposed a graph neural network-based cooling load prediction method to integrate the associations and temporal information of the features. Using the weighted mask mechanism, a knowledge embedded dynamic association graph is constructed to handle the time-varying feature associations rationally. By introducing empirical knowledge and adjusting the graph structure automatically according to the real-time data, the proposed method can provide accurate cooling load prediction. Comprehensive experiments are carried out on the onsite data, and the experimental results show the practicability and effectiveness of the proposed method for cooling load prediction.(c) 2022 Elsevier B.V. All rights reserved.
Keywords:
Building energy saving
Cooling load prediction
Dynamic data association
Graph neural network
Knowledge embedding
Journal
IF:
7.1
Papers:
1.6W
Citations:
6.8W
Organization
Cited Papers
An improved office building cooling load prediction model based on multivariable linear regression.png)
ENERGY AND BUILDINGS
ENERGY AND BUILDINGSIF7.1
PREAI
ShareSaveBuilding operation and energy performance: Monitoring, analysis and optimisation toolkitAPPLIED ENERGY
APPLIED ENERGYIF11
PREAI
ShareSaveAttention-based interpretable neural network for building cooling load predictionAPPLIED ENERGY
OAAI
APPLIED ENERGYIF11
OAAIShareSaveNovel dynamic forecasting model for building cooling loads combining an artificial neural network and an ensemble approachAPPLIED ENERGY
APPLIED ENERGYIF11
PREAI
ShareSaveTherapeutic efficacy of intravenous immunoglobulin preparations depends on the immunoglobulin G dimers: studies in experimental immune thrombocytopeniaBloodOAAI
BloodIF0
OAAIShareSaveA long short-term memory artificial neural network to predict daily HVAC consumption in buildingsENERGY AND BUILDINGS
ENERGY AND BUILDINGSIF7.1
PREAI
ShareSave
View more