Predicting success of energy savings interventions and industry type using smart meter and retrofit data from thousands of non-residential buildings-Reference-Cited by-同舟云学术

Predicting success of energy savings interventions and industry type using smart meter and retrofit data from thousands of non-residential buildings

Published:2017-11-08 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 4th ACM International Conference on Systems for Energy-Efficient Built Environments
language:
Short-container-title:

Author:

Miller Clayton¹

Affiliation:

1. National University of Singapore (NUS), Singapore and ETH Zürich, Zürich, Switzerland

Funder

Institute of Technology in Architecture (ITA) Fellowship from the ETH Zürich

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3137133.3137160

Reference10 articles.

1. Adrian Albert and Mehdi Maasoumy. 2016. Predictive segmentation of energy consumers. Applied Energy 177 (Sept. 2016) 435--448. Adrian Albert and Mehdi Maasoumy. 2016. Predictive segmentation of energy consumers. Applied Energy 177 (Sept. 2016) 435--448.

2. Smart Meter Driven Segmentation: What Your Consumption Says About You

3. Philipp Geyer Arno Schluter and Sasha Cisar. 2016. Application of clustering for the development of retrofit strategies for large building stocks. Advanced Engineering Informatics (2016). http://www.sciencedirect.com/science/article/pii/S1474034616300167 Philipp Geyer Arno Schluter and Sasha Cisar. 2016. Application of clustering for the development of retrofit strategies for large building stocks. Advanced Engineering Informatics (2016). http://www.sciencedirect.com/science/article/pii/S1474034616300167

4. Analyzing Energy Usage on a City-scale using Utility Smart Meters

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Interpretable domain-informed and domain-agnostic features for supervised and unsupervised learning on building energy demand data;Applied Energy;2024-04

2. Applications of Data-Driven Policymaking in the Local Energy Transition: A Multiple-case Study in the Netherlands;Electronic Participation;2022

3. The impact of negative list policy on sectoral structure: Based on complex network and DID analysis;Applied Energy;2020-11

4. State-of-the-art on research and applications of machine learning in the building life cycle;Energy and Buildings;2020-04