Weather clustering for machine learning-based hourly building energy prediction models at design phase: Fid-Bau Portal

Weather clustering for machine learning-based hourly building energy prediction models at design phase

Zhan, Dongxue / Qin, Shaoxiang / Hassan, Ibrahim Galal

Access

Download

Page navigation

Document information

Similar titles

Export, share and cite

Weather clustering for machine learning-based hourly building energy prediction models at design phase

Zhan, Dongxue / Qin, Shaoxiang / Hassan, Ibrahim Galal

Weather clustering for machine learning-based hourly building energy prediction models at design phase

Zhan, Dongxue / Qin, Shaoxiang / Hassan, Ibrahim Galal

Access

Download

Page navigation

Document information

Similar titles

Export, share and cite

Document information

Title:

Weather clustering for machine learning-based hourly building energy prediction models at design phase

Additional title:

Energy and Buildings

Contributors:

Zhan, Dongxue (author) / Qin, Shaoxiang (author) / Hassan, Ibrahim Galal (author)

Published in:

Energy and Buildings ; 329 ; 115308

Publication date:

2025-02-01

ISSN:

0378-7788

DOI:

https://doi.org/10.1016/j.enbuild.2025.115308

Type of media:

Article (Journal)

Type of material:

Electronic Resource

Language:

English

Licence:

http://creativecommons.org/licenses/by/4.0/

Dong Li / Xiaofei Ren | DOAJ | 2022

Free access

Building’s hourly electrical load prediction based on data clustering and ensemble learning strategy

Li, Kangji / Zhang, Jinxing / Chen, Xu et al. | Elsevier | 2022

Predicting Hourly Building Energy Usage

Kreider, J. F. / Haberl, J. S. | British Library Online Contents | 1994

North American and European Hourly Based Weather Data and Methods for HVAC Building Energy Analyses and Design by Simulation

Levermore, G. J. / Doylend, N. O. | British Library Online Contents | 2002

Hourly energy consumption prediction of an office building based on ensemble learning and energy consumption pattern classification

Dong, Zhenxiang / Liu, Jiangyan / Liu, Bin et al. | Elsevier | 2021