.png){kind=icon}
返回
Automated machine learning-based building energy load prediction method
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jobe.2023.108071.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The application of data-driven building energy load prediction technologies remains a timeconsuming effort, since it highly relies on human expertise to train data-driven building energy load prediction models. To address this issue, this study proposes an automated machine learning based method which can develop accurate data-driven building energy load prediction models with little human assistance. The potential of six representative automated machine learning frameworks (AutoWeka, H2O, TPOT, AutoGluon, FLAML and AutoKeras) are investigated through utilizing them to forecast 1-h ahead heating, cooling and electrical loads of three real world buildings. The results show that the proposed method outperforms manual modeling. The accuracy of the proposed method increases by 1.10%-18.66% compared to manual modeling. It is also demonstrated that AutoGluon and FLAML can obtain high prediction accuracy using short training times. Moreover, AutoWeka, H2O and TPOT usually need longer training times than AutoGluon and FLAML to develop high-accuracy prediction models. As for AutoKeras, it doesn't show high accuracy on building energy load prediction. This study provides guidelines for making data-driven building energy load prediction technologies easier to use in practical applications.
Keyword:
Building energy load prediction
Data-driven models
Automated machine learning
Manual modeling
Building energy efficiency enhancement
期刊
IF:
7.4
论文数:
1.7W
被引数:
6.6W
机构
引用论文
分享收藏Graph convolutional networks-based method for estimating design loads of complex buildings in the preliminary design stage.png)
APPLIED ENERGY
APPLIED ENERGYIF11
PREAI
分享收藏
Elastic weight consolidation-based adaptive neural networks for dynamic building energy load prediction modelingENERGY AND BUILDINGS
ENERGY AND BUILDINGSIF7.1
PREAI
分享收藏Deep learning-based feature engineering methods for improved building energy prediction基于深度学习的特征工程方法改进建筑能耗预测APPLIED ENERGY
APPLIED ENERGYIF11
PREAI
分享收藏Problem of data imbalance in building energy load prediction: Concept, influence, and solutionAPPLIED ENERGY
APPLIED ENERGYIF11
PREAI
分享收藏Energy consumption predicting model of VRV (Variable refrigerant volume) system in office buildings based on data mining基于数据挖掘的办公建筑VRV系统能耗预测模型ENERGY
ENERGYIF9.4
PREAI
分享收藏
查看更多内容