Data analytics for simplifying thermal efficiency planning in cities.
More than 44% of building energy consumption in the USA is used for space heating and cooling, and this accounts for 20% of national CO2emissions. This prompts the need to identify among the 130 million households in the USA those with the greatest energy-saving potential and the associated costs of the path to reach that goal. Whereas current solutions address this problem by analysing each building in detail, we herein reduce the dimensionality of the problem by simplifying the calculations of energy losses in buildings. We present a novel inference method that can be used via a ranking algorithm that allows us to estimate the potential energy saving for heating purposes. To that end, we only need consumption from records of gas bills integrated with a building's footprint. The method entails a statistical screening of the intricate interplay between weather, infrastructural and residents' choice variables to determine building gas consumption and potential savings at a city scale. We derive a general statistical pattern of consumption in an urban settlement, reducing it to a set of the most influential buildings' parameters that operate locally. By way of example, the implications are explored using records of a set of (N= 6200) buildings in Cambridge, MA, USA, which indicate that retrofitting only 16% of buildings entails a 40% reduction in gas consumption of the whole building stock. We find that the inferred heat loss rate of buildings exhibits a power-law data distribution akin to Zipf's law, which provides a means to map an optimum path for gas savings per retrofit at a city scale. These findings have implications for improving the thermal efficiency of cities' building stock, as outlined by current policy efforts seeking to reduce home heating and cooling energy consumption and lower associated greenhouse gas emissions.
Department of Civil and Environmental Engineering, University of California at Irvine, Irvine, CA 92617, USA.
Mohammad Javad
MJ
Abdolhosseini Qomi
Zachary Department of Civil Engineering, Texas A&M University, TX 77843, USA.
Arash
A
Noshadravan
Department of Civil and Environmental Engineering, 77 Massachusetts Avenue, Cambridge, MA 02139, USA.
Jake M
JM
Sobstyl
Engineering Systems Division, 77 Massachusetts Avenue, Cambridge, MA 02139, USA.
Jameson
J
Toole
Department of Urban Studies and Planning, 77 Massachusetts Avenue, Cambridge, MA 02139, USA.
Joseph
J
Ferreira
Department of Civil and Environmental Engineering, 77 Massachusetts Avenue, Cambridge, MA 02139, USA MSE2 MIT-CNRS Joint Laboratory, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA Centre Interdisciplinaire des Nanosciences de Marseille, CNRS and Marseille Université, Campus de Luminy, Marseille, 13288 Cedex 09, France.
Roland J-M
RJ
Pellenq
Department of Civil and Environmental Engineering, 77 Massachusetts Avenue, Cambridge, MA 02139, USA Department of Urban Studies and Planning, 77 Massachusetts Avenue, Cambridge, MA 02139, USA.
Franz-Josef
FJ
Ulm
Department of Civil and Environmental Engineering, 77 Massachusetts Avenue, Cambridge, MA 02139, USA Engineering Systems Division, 77 Massachusetts Avenue, Cambridge, MA 02139, USA martag@mit.edu.
Marta C
MC
Gonzalez
D003246:Conservation of Energy Resources / Q000379:methods*; D060389:Construction Industry / Q000379:methods*; D006358:Hot Temperature; D008404:Massachusetts; D008962:Models, Theoretical; D014506:Urban Renewal / Q000379:methods*
massive–passive data analytics; probabilistic model reduction; response surface methodology; strategic gas consumption planning