A Deep-Learning-Based Meta-Modeling Workflow for Thermal Load Forecasting in Buildings: Method and a Case Study

A Deep-Learning-Based Meta-Modeling Workflow for Thermal Load Forecasting in Buildings: Method and a Case Study

This paper proposes a meta-modeling workflow to forecast the cooling and heating loads of buildings at individual and district levels in the early design stage. Seven input variables, with large impacts on building loads, are selected for designing meta-models to establish the MySQL database. The lo...

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Bibliographic Details
Main Authors: Yuhao Zhou, Yumin Liang, Yiqun Pan, Xiaolei Yuan, Yurong Xie, Wenqi Jia
Format: Article
Language:English
Published: MDPI AG 2022-02-01
Series:Buildings
Subjects:
building load forecasting
meta-modeling
deep learning
CNN
Seq2Seq
district
Online Access:https://www.mdpi.com/2075-5309/12/2/177

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