亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

Comprehensive transferability assessment of short-term cross-building-energy prediction using deep adversarial network transfer learning

可转让性 人工智能 学习迁移 深度学习 计算机科学 数据挖掘 机器学习 人工神经网络 样板房 相似性(几何) 物理 图像(数学) 罗伊特 量子力学
作者
Guannan Li,Yubei Wu,Sungmin Yoon,Xi Fang
出处
期刊:Energy [Elsevier BV]
卷期号:299: 131395-131395 被引量:11
标识
DOI:10.1016/j.energy.2024.131395
摘要

Data-driven models are widely used for building-energy predictions (BEP). In practice, these models may fail when the available data on the target building is insufficient. Transfer learning (TL), where useful knowledge from information-rich source buildings with sufficient data is learned to enhance the prediction of information-poor buildings with data shortages, can address the problem of poor prediction performance caused by insufficient data. However, an important issue is finding suitable information-rich buildings to maximize the advantages of TL in cross- BEP. To address this issue, this study explored the selection of source buildings from three perspectives: building-energy data similarity between source and target buildings, building information characteristics, and the volume of training data. The impact of these three factors on the performance improvement of cross- BEP was assessed in a data-centric manner. Based on our previous studies, we selected a deep adversarial neural network (DANN) as the TL strategy for cross- BEP and systematically investigated the performance improvement and transferability of DANN from multiple perspectives of both post-hoc and ex-ante analysis. The Building Data Genome Project datasets were used for validation. Thirty-six buildings of six types and 180 source-target building pairs were considered. Our results demonstrated that DANN could effectively improve model performance by 40-90% and 20%-80% compared to non-optimized LSTM and parameter-optimized LSTM. When the same type and location source-target building pairs were only considered, the DTW index showed a relative strong negative linear correlation with the DANN prediction performance improvement, and the goodness of fitting is around 0.80. For building energy data within one year considered, DANN should be trained using no less than 6-month source domain data and no more than 4-week target domain data to improve transferability and reduce the cross-building energy prediction error.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
5秒前
Geralt发布了新的文献求助10
6秒前
科研通AI2S应助无情的君浩采纳,获得10
13秒前
18秒前
1分钟前
王佳佳发布了新的文献求助10
1分钟前
1分钟前
1分钟前
JUNE发布了新的文献求助10
1分钟前
feiying完成签到,获得积分10
1分钟前
1分钟前
充电宝应助王佳佳采纳,获得10
1分钟前
小丑完成签到,获得积分10
1分钟前
时不我待C发布了新的文献求助10
1分钟前
1分钟前
Jasper应助阳光的八宝粥采纳,获得10
2分钟前
2分钟前
2分钟前
美满的豆芽完成签到 ,获得积分10
2分钟前
顺利白竹完成签到 ,获得积分10
2分钟前
科研通AI2S应助科研通管家采纳,获得20
2分钟前
科研通AI5应助科研通管家采纳,获得10
2分钟前
2分钟前
fzhou完成签到 ,获得积分10
3分钟前
李健应助rerorero18采纳,获得10
3分钟前
3分钟前
王佳佳发布了新的文献求助10
3分钟前
懒惰扼杀激情完成签到 ,获得积分10
3分钟前
深情安青应助王佳佳采纳,获得10
3分钟前
xzw完成签到,获得积分10
3分钟前
3分钟前
4分钟前
rerorero18发布了新的文献求助10
4分钟前
ding应助帅气冰蝶采纳,获得10
4分钟前
4分钟前
布同完成签到,获得积分10
4分钟前
赘婿应助半分甜采纳,获得10
4分钟前
半分甜完成签到,获得积分10
4分钟前
4分钟前
搜集达人应助科研通管家采纳,获得10
4分钟前
高分求助中
Africanfuturism: African Imaginings of Other Times, Spaces, and Worlds 3000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 2000
Electron microscopy study of magnesium hydride (MgH2) for Hydrogen Storage 1000
Learning to Listen, Listening to Learn: Music Perception and the Psychology of Enculturation 700
Structural Equation Modeling of Multiple Rater Data 700
全球膝关节骨性关节炎市场研究报告 555
Exhibiting Chinese Art in Asia: Histories, Politics and Practices 550
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3894954
求助须知:如何正确求助?哪些是违规求助? 3438686
关于积分的说明 10808113
捐赠科研通 3163628
什么是DOI,文献DOI怎么找? 1747668
邀请新用户注册赠送积分活动 844085
科研通“疑难数据库(出版商)”最低求助积分说明 787809