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\begin{document}
\title{PUI2016 Extra Credit}
\author[ ]{Ozgur L. Akkas}
\author[ ]{Federica B. Bianco}
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\textbf{Ozgur L. Akkas}\\
\textbf{PUI 2016}\\
\textbf{Extra Credit Assignment}\\
\textbf{12/15/2016}\\
\textbf{~}\\
\textbf{Problem Description:~}\\
\textbf{~}\\
Does Leadership in energy star buildings in NYC show substantial energy
savings compared to those non-energy star buildings? Looking at the 2013
and 2014 NYC energy and water disclosure benchmark data, the City's
building can be evaluated based on the grade level they received. Energy
start buildings can help reduce energy consumption and reduce
environmental impact once a building meets Environmental Protection
Agency regulations. According to the Department of Energy, to earn a
certification, a building must earn Energy Star score of 75 or higher
whereas a score 50 or above reflect the median energy
performance\protect\hyperlink{_ftn1}{{[}1{]}}. To see whether energy
star buildings units, this report compared buildings with the below
median and those above the median.\\
\textbf{Data:~}\\
New York City Benchmarking provides water and energy use data as CSV
files collected during the year 2013 and 2014. In the analysis, weather
normalized data is chosen as the dependent variable and the reported
building floor area as the independent variable. Then, NYC zip codes
data given in the same tables were used to stack the information to make
statistical tests. Links to data are given in the Appendix.\\
\textbf{Analysis:~}\\
This analysis uses ARIMA method for evaluation of the data sets. To find
out the variance, adjusted R\^{}2 value is calculated. R value was
calculated to see whether the median line passes as many data points or
not. When R equals zero, it means not the fit does not pass as many
points and when R equals one line passes as many point. The Chi square
is also calculated to evaluate the data error estimate to acceptable or
too good. Chi of one means error estimate is acceptable and zero means
error estimate is too unrealistic.\\
\textbf{Results}\\
For energy star rated building, the adjusted R\^{}2 for 2013 data is
0.978 which means the linear fit is passing through majority of the
data. Same conclusion can be made for the 2014 data with adjusted R\^{}2
equals 0.975. For buildings with greater than 50 stars, the R is 0.980
for 2013 and 0.978 for 2014, respectively. In the appendix below both
OLS plots show data below the median and above the median energy data
for two consecutive years.\\
\textbf{Recommendations for Further Analysis:}\\
Clustering of the data with the energy use intensity (EUI) and apply a
geospatial distance map to see how energy star certification has
impacted various City neighborhoods. Also, producing a time series
analysis beginning from a certain year when energy star certification
for building has been issued can be another area to be studied to
measure the impact on buildings' total green house gas emissions and the
future trend. ARIMA can be used to make predictions and estimate.\\
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Appendix:\\
Benchmarking Data CSV files\\
\url{http://www.nyc.gov/html/gbee/html/plan/ll84\_scores.shtml}\\
Article about LEED Energy Consumption:\\
\url{https://www.greenbiz.com/blog/2014/09/10/new-usgbcember-strategies-study-unpacks-leed-plaque}\\
Figure-1: Buildings with More Than 50 Energy Star Rating in 2013\\
~\\
Figure-1: Buildings with More Than 50 Energy Star Rating in 2014\\
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\protect\hyperlink{_ftnref1}{{[}1{]}}~ Department of Energy:
\url{https://www.energystar.gov/buildings/facility-owners-and-managers/existing-buildings/earn-recognition/energy-star-certification}\\
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