Building energy performance prediction using neural networks
Author:
Funder
Seventh Framework Programme
Publisher
Springer Science and Business Media LLC
Subject
General Energy
Link
http://link.springer.com/article/10.1007/s12053-017-9524-5/fulltext.html
Reference29 articles.
1. Ahmad, A. S., Hassan, M. Y., Abdullah, M. P., Rahman, H. A., Hussin, F., Abdullah, H., & Saidur, R. (2014). A review on applications of ANN and SVM for building electrical energy consumption forecasting. Renewable and Sustainable Energy Reviews, 33, 102–109.
2. Ben-Nakhi, A. E., & Mahmoud, M. A. (2004). Cooling load prediction for buildings using general regression neural networks. Energy Conversion and Management, 45(13–14), 2127–2141.
3. Bowden, G. J., Dandy, G. C., & Maier, H. R. (2005). Input determination for neural network models in water resources applications. Part 1—background and methodology. Journal of Hydrology, 301(1–4), 75–92.
4. Buratti, C., Barbanera, M., & Palladino, D. (2014). An original tool for checking energy performance and certification of buildings by means of artificial neural networks. Applied Energy, 120, 125–132.
5. Castilla, M., Álvarez, J. D., Ortega, M. G., & Arahal, M. R. (2013). Neural network and polynomial approximated thermal comfort models for HVAC systems. Building and Environment, 59, 107–115.
Cited by 25 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. A Review of Research on Building Energy Consumption Prediction Models Based on Artificial Neural Networks;Sustainability;2024-09-07
2. Building simulation in adaptive training of machine learning models;Automation in Construction;2024-09
3. Experimental validation of ventilated double-skin façades aided by neural networks and thermal modelling for heating demand reduction;Building and Environment;2024-05
4. A protocol for developing and evaluating neural network-based surrogate models and its application to building energy prediction;Renewable and Sustainable Energy Reviews;2024-04
5. Improving the prediction of energy performance of building using electromagnetic field optimization algorithm;International Journal of Low-Carbon Technologies;2024
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3