An improved transfer learning strategy for short-term cross-building energy prediction using data incremental

IF 6.1 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY Building Simulation Pub Date : 2023-11-09 DOI:10.1007/s12273-023-1053-x

Guannan Li, Yubei Wu, Chengchu Yan, Xi Fang, Tao Li, Jiajia Gao, Chengliang Xu, Zixi Wang

引用次数: 0

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

基于数据增量的短期跨建筑能耗预测的改进迁移学习策略

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Building Simulation THERMODYNAMICS-CONSTRUCTION & BUILDING TECHNOLOGY

CiteScore

10.20

自引率

16.40%

发文量

审稿时长

>12 weeks

期刊介绍： Building Simulation: An International Journal publishes original, high quality, peer-reviewed research papers and review articles dealing with modeling and simulation of buildings including their systems. The goal is to promote the field of building science and technology to such a level that modeling will eventually be used in every aspect of building construction as a routine instead of an exception. Of particular interest are papers that reflect recent developments and applications of modeling tools and their impact on advances of building science and technology.

期刊最新文献

Evolving multi-objective optimization framework for early-stage building design: Improving energy efficiency, daylighting, view quality, and thermal comfort An integrated framework utilizing machine learning to accelerate the optimization of energy-efficient urban block forms Exploring the impact of evaluation methods on Global South building design—A case study in Brazil Mitigation of long-term heat extraction attenuation of U-type medium-deep borehole heat exchanger by climate change Developing an integrated prediction model for daylighting, thermal comfort, and energy consumption in residential buildings based on the stacking ensemble learning algorithm