Literature review and related previous
work
\label{literature-review-and-related-previous-work}
Academia
\label{academia}
The problem of defining an energy performance metric for buildings has
been extensively explored in academia and many different approaches have
been proposed. Borgstein et al . (2016) [3] made a
comprehensive literature review of these methods and divided them into
four main groups: Engineering Calculations, Simulations, Statistical
methods and Machine Learning methods. The authors also highlight
differences between Calculated and Measured energy ratings, as defined
by the ISO 16346:2013, and point to the different possible reference
systems (baselines) when defining an energy performance metric:
Historical energy performance, Typical performance of similar buildings
(empirical), Expected energy performance, Potential energy performance
and Required performance (norms or standards). The work of Borgstein
et al . (2016) [3] characterizes well the landscape in which
the performance metric for the Energy Snapshot is being developed and
also illustrates the complexity related to that goal.
In order to better understand the main aspects contributing to the
energy performance of a building, one can refer to the Buildings and
Communities Programme of the International Energy Agency (IEA EBC). Its
Annex 53 (2013) [4] investigated the main aspects influencing energy
consumption in buildings and grouped them in six major groups: Climate,
Building envelope, Building systems, Operations & maintenance, Occupant
behaviour, Indoor environmental conditions. Those were the aspects
considered to select and engineer features to predict buildings’ energy
use intensity, to identify peer groups and to build the energy
performance metric.
Kontokosta et al . (2015) [5] gave an important contribution
to the understanding of the data from LL84 data by building an
interactive web-based visualization for some of its parameters at the
level of individual buildings in New York City. The purpose of this work
was to provide a better understanding of the energy usage around the
city. Besides that, even more important contributions from this work
were the models built to extrapolate and generalize the LL84 data to
estimate the natural gas and electricity consumption for all buildings
in NYC. The assessment of the model’s’ accuracy was made by comparing
its results with the aggregate natural gas and electricity consumption,
per year, on the zip code level. The median absolute percent error
(median APE) for electricity was 10.75, meaning that half of the
predicted zip codes were within 10.75% of the correct value. For the
natural gas, the median APE was 30.
Kontokosta (2015) [6] refers to Sartor et al. (2000) [7]
to separate the approaches for building energy performance metrics in
four groups: simulation models, point systems, end-use metrics, and
regression models. From those, the author argues that the regression
approach might be the most appropriate to build an energy performance
metric specific for New York City (NYC). The model proposed takes as
reference the typical performance of similar buildings (as predicted by
a regression model) and compares it to the actual energy use (Energy Use
Intensity) of a given building. For Office buildings reporting their
annual energy consumption data for the year 2010 in the standards of NYC
LL84, the regression model was able to explain 33% of the variance in
the Weather Normalized Energy Use Intensity. In the present work, a
similar approach was used not only for Office buildings, but also for
Multifamily Housing buildings.
Hsu (2015) [8] explored different regularization techniques to
identify key variables and interactions to build a regression model to
predict Site EUI of NYC Office and Multifamily Housing buildings. The
model included data from LL84, PLUTO, CoStar, U.S. Census and American
Community Survey (ACS). The author found that a hierarchical group-lasso
regularization significantly outperformed ridge, lasso, elastic net and
ordinary least squares approaches in terms of prediction accuracy.
Besides that, the results showed that some of the most important
variables for the Multifamily Housing model were related to type of
energy (percentage of electricity), the age of the building, use of
space and ethnicity in the building’s census tract. For Office
buildings, the main effects were also from the type of energy source
used (electricity, natural gas, steam, etc.), but an interesting finding
was that information from the Census and ACS describing the composition
of surrounding multifamily units was also included among the main
predictors. For the present analysis, the features selection and
engineering step to model EUI was also assisted by these findings,
however, in order to emphasize individual building characteristics and
not characteristics from the neighbourhood, census tract, or zip code
level, which could compromise the fairness of the scoring model, only
data from LL84 and PLUTO (which are specific to each building) were used
in the model.
In another effort to identify the main features determining Energy Use
Intensity in Multifamily Housing buildings in NYC, Ma and Cheng (2016)
[9] used a Random Forest model to predict Average Site EUI at the
level of Census Block Groups. The features analysed came from the PLUTO
database, the 2013 ACS 5-year estimates from the U.S. Census Bureau,
besides the LL84 dataset. These features were representative of seven
different categories: Building, Economy, Education, Environment,
Population and household, Surrounding and Transportation. The Mean
Square Error of the Random Forest model (with optimized parameters) was
0.773, smaller than for other models tried by the authors such as
Multiple Linear Regression (MSE = 0.997), Lasso (MSE = 0.872), and
Support Vector Machines (MSE = 0.830). The most influential variables
were found to be mostly related to the categories Building, Education
and Economy. This work not only helped in defining important features to
predict EUI but also in deciding to use a random forest model as a
nonlinear approach to that task.
Other Cities
\label{other-cities}
Especially in the context of mitigating GHG emissions to fight climate
change, in recent years many city governments have started to implement
policies targeting energy efficiency in the built environment. Trencher
et al. (2016) [10] examined ten programmes to advance energy
efficiency and retrofitting of existing private sector buildings in C40
cities in Asia-Pacific (Melbourne, Sydney, Hong Kong, Singapore and
Tokyo) and in the U.S. (Houston, New York, San Francisco, Philadelphia
and Seattle). The study identified six distinguished policy models, four
mandatory (Benchmarking, Periodical energy efficiency auditing or
retro-commissioning, Energy efficiency standards, Cap-and-trade), and
two voluntary (Capacity building, Friendly competition). Overall, the
environmental impacts of such policies were found particularly slow to
emerge and plagued with attribution challenges. The authors found
limited evidence of benchmarking programme effectiveness in reducing
energy consumption in the short-term, but some indication of mid-term
outcomes. Driven by unique local circumstances, the cap-and-trade model
stood out by fostering large, sustained and attributable GHG emission
reductions and retrofitting. Finally, the authors highlight the
complementary aspect of voluntary and mandatory programmes and potential
for benchmarking programmes to later transition to models mandating
performance improvements, such as cap-and-trade.
Figure 1 shows a map with 24 U.S cities that had some sort of energy
benchmarking policy for buildings, as of February of 2017 [11]. It
is worth noticing that depending on the city, not all types of buildings
are included in the benchmarking policy and also there might or might
not be something beyond the benchmarking in place, such as
retro-commissioning or auditing. From the cities shown in Figure 1, some
like Philadelphia, Seattle and Chicago, similarly to New York, have web
visualization tools to make the data reported by each of the compliant
buildings publicly available and easily obtainable.