Surrogate modeling for long-term and high-resolution prediction of building thermal load with a metric-optimized KNN algorithm

Author:

Liang YuminORCID,Pan Yiqun,Yuan Xiaolei,Jia Wenqi,Huang Zhizhong

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Transportation,Renewable Energy, Sustainability and the Environment,Building and Construction,Civil and Structural Engineering

Reference40 articles.

1. BP p.l.c., Statistical Review of World Energy 2021 : 70th edition. https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energy-economics/statistical-review/bp-stats-review-2021-full-report.pdf, 2021 (accessed 16 March 2022).

2. Dynamic forecast of cooling load and energy saving potential based on Ensemble Kalman Filter for an institutional high-rise building with hybrid ventilation;Hou;Build. Simul.,2020

3. A regression-based framework to examine thermal loads of buildings;Najjar;J Clean Prod,2021

4. An improved input variable selection method of the data-driven model for building heating load prediction;Ling;Journal of Building Engineering,2021

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