In this section
Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.
@article{Molina-Solana:2016:10.1016/j.rser.2016.11.132,
author = {Molina-Solana, M and Ros, M and Ruiz, MD and Gómez-Romero, J and Martin-Bautista, MJ},
doi = {10.1016/j.rser.2016.11.132},
journal = {Renewable and Sustainable Energy Reviews},
pages = {598--609},
title = {Data science for building energy management: A review},
url = {http://dx.doi.org/10.1016/j.rser.2016.11.132},
volume = {70},
year = {2016}
}
TY - JOUR
AB - The energy consumption of residential and commercial buildings has risen steadily in recent years, an increase largely due to their HVAC systems. Expected energy loads, transportation, and storage as well as user behavior influence the quantity and quality of the energy consumed daily in buildings. However, technology is now available that can accurately monitor, collect, and store the huge amount of data involved in this process. Furthermore, this technology is capable of analyzing and exploiting such data in meaningful ways. Not surprisingly, the use of data science techniques to increase energy efficiency is currently attracting a great deal of attention and interest. This paper reviews how Data Science has been applied to address the most difficult problems faced by practitioners in the field of Energy Management, especially in the building sector. The work also discusses the challenges and opportunities that will arise with the advent of fully connected devices and new computational technologies.
AU - Molina-Solana,M
AU - Ros,M
AU - Ruiz,MD
AU - Gómez-Romero,J
AU - Martin-Bautista,MJ
DO - 10.1016/j.rser.2016.11.132
EP - 609
PY - 2016///
SN - 1364-0321
SP - 598
TI - Data science for building energy management: A review
T2 - Renewable and Sustainable Energy Reviews
UR - http://dx.doi.org/10.1016/j.rser.2016.11.132
UR - http://hdl.handle.net/10044/1/48054
VL - 70
ER -
Data Science Institute
William Penney Laboratory
Imperial College London
South Kensington Campus
London SW7 2AZ
United Kingdom
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