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Data analysis for the Acadia/Fairview Residence Zemcov, Clare 2014-10-10

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 UBC Social Ecological Economic Development Studies (SEEDS) Student ReportClare ZemcovPLAN 597: Planning for Water Resource ManagementAssignment 1: Data Analysis for the Acadia/Fairview ResidencePLAN 597October 14, 201411451696University of British Columbia Disclaimer: “UBC SEEDS provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Coordinator about the current status of the subject matter of a project/report”.                     PLAN 597: Planning for Water Resource Management Assignment 1: Data Analysis for the Acadia/Fairview Residence                    Author: Clare Zemcov October 10, 2014 Clare Zemcov October 10, 2014   2 Data collected for this assignment was for the Acadia/Fairview Residential building on the UBC campus in Vancouver, BC.  The data was provided by the UBC Water and Energy Department and available data spanned from January 2010 to September 2014 at a monthly frequency.  Data for 10 of the 57 months was not available when the water meter measurement at the building was not collected.   The Fairview Residence building offers townhouse-style suites on the southeast portion of the UBC campus. Water saving faucets and toilets were installed within the building in the summers of 2013 and 2014.   The data available was composed of the following: • Date the measurement was collected • The base meter reading (usually 15 or 25 m3) • The total cumulative meter usage reading  In order to analyze this data, it was necessary to subtract the base meter readings from the change in cumulative water consumption and convert the readings to cubic feet.  An example is as follows:  Table 1. Example data for the Fairview Residence Building Date%Base%Reading%(m3)%Change%in%Base%Reading%(m3)%Cumulative%Meter%Reading%(m3)%Change%in%Meter%Reading%(m3)%Change%in%Water%Consumption%(m3)%Change%in%Water%Consumption%(ft3)%2014%06%17( 25( 10( 74395( 400( 410( 14478.93(2014%05%21( 15((73995(( ((Black font – provided data    Blue font – Calculated data  Example Calculations:  !"#$!!"#$%&'!(2014 − 06 − 17) !− !!"#$!!"#$%&'!(2014 − 05 − 21) != !25! − !15! = !10 m3  !ℎ!"#$!!"!!"#"$%&'()! "#"$!!"#$%&'! = !74,395!– !73,995! = !400 m3  !"#$%!!ℎ!"#$!!"! "#$%!!"#$%&'!"#$! = !10 + !400 = !410 m3  !"#$%&'("#!!"!!ℎ!"#$!!"!Water&Consumption&to&ft3 = 410 * 35.314475 = 14,478.93 ft3  As shown on the data attached, the following statistics were calculated: • Mean = 18,859 ft3 = 534 m3 • Median = 18,893 ft3 = 535 m3 • Mode = 18,893 ft3 = 535 m3 • Variance = 8,745,561 ft3 = 247,648 m3 • Standard Deviation = 2957 ft3 = 84 m3  The histogram of the frequency of ranges of water consumption is show attached as Figure 1.  The distribution is asymmetrically right-skewed with the most frequent water consumption ranging between 18,400-19,999 ft3 (~520 m3 – 566 m3).  Therefore, there is a tendency for water consumption at the Fairview building to be located around the mean usage or less on a monthly basis.  With additional data provided after the final installation of water-saving measures during Clare Zemcov October 10, 2014   3 the summer of 2014, water usage should decrease and this skew may become more asymmetric and right-skewed as the mean decreases over time.   A time-series analysis was conducted for the data and is shown on Figure 2.  As previously stated, the installation of water-saving faucets and toilets were installed in the summer of 2013 and 2014.  In this figure, those months have been shaded in red.   In order to determine seasonal patterns a third figure was composed (Figure 3). In this figure, the yearly consumptive water use was plotted so that seasonal fluctuations could be discerned.  Although there are data gaps during the summers of 2011 to 2014, general trends are observed throughout the remainder of the year.  There are four months of the year that in which peak use occurs: February, April, August and October.  The cycles are generally sinusoidal throughout the year.   Data between the months of January to May were available for all five years, and based on those results, a decrease in water consumption is observable from 2010 through to 2014.  Since data is spotty for the remainder of the year, further observations are not possible.   Peak water use decreased from 26,132 ft3 in February 2010 to 23,130 ft3 in April 2014. That is a decrease of approximately 3,000 ft3 at the peak occurrence of the year.  In addition, a sum of the total water consumption per year is as follows:  Table 2. Total Water Consumption per year between 2010 and 2014 (2010% 2011% 2012% 2013% 2014%Total(Water(Consumption((ft3)( 207119( 196348( 144966( 184518( 153441(Total(Water(Consumption((m3)( 5865( 5560( 4105( 5225( 4345(Months(of(data( Feb(%(Dec( Jan(%(Dec( Jan(%(Dec( Jan(%(Dec( Jan(%(Sep( However, this data must be used conservatively for 2010 and 2014 since only data between February and December 2010 and from January to September 2014 was collected and used in this calculation. Therefore, the total water consumption for 2010 and 2014 does not represent cumulative water consumption for an entire year and is not directly comparable to the 2011-2013 results.  Based on the total water consumption per year, and the fact that there is one remaining months worth of data for 2010 and three remaining months worth of data for 2014, there is no observable significant different between the total water consumption after the installation of the new water conserving fixtures, especially with the year 2012 having such a low total water consumption compared to the other years.    In order to statistically determine if the water conservation before and after the implementation of some of the faucets and toilets are different, a t-test value was calculated for the data. A t-test will determine if the difference between the two means in relation to the variation of the data is significant. The results provide a calculated t value of 1.89. When a t table is consulted, at p=0.05 and df=47 (round to 40), the tabulated value is 2.02.  Since the calculated t value is less than the tabulated value, the means are not considered significantly different.    Clare Zemcov October 10, 2014   4 Table 3. Statistical t-test Analysis before and after Water Conservation Fixtures were Installed at the Acadia Fairview Residence.  (February(2010(to(June(2013(June(2013(to(September(2014(Mean( 19358.75( 17682.46(Variance( 8750865.46( 7280531.06(Sample(Size( 33( 14(Variance(between(the(two(means((Sd2)(785216(Square(root(of(Sd2( 886(Calculated(t(value( 1.89(Tabulated(t(test(value((p=0.05)(2.02( However, since this test was completed using data from 2013 and the first half of 2014, not all of the water conserving fixtures had been installed.  Therefore, with additional and complete data for the years of 2015 onwards, there is a probability that water consumption will decrease and the t test results will show that the means before and after installation are significantly different. In addition, various months within 2010 and 2014 have missing data (as discussed above), which will influence the mean values for these years.  Accumulating and analyzing more data will allow for a greater accuracy in the results of further t-tests.    I appreciate the continued effort by water management team to be proactive about water conservation at UBC.  Although, this data does not currently reflect a decrease in water consumption at the Fairview/Acadia Residence on the campus, the data provided for this analysis has limitations.  These limitations include:  • Only four years worth of data prior to initial installation of water conserving fixtures • The installation of water conserving fixtures over two consecutive summers.   • The final installation of these fixtures in the summer of 2014, and data only available until September 2014.  Not enough data after installation was available to calculate meaningful statistical values of difference. • Summer data not available for the years 2011 to 2013, therefore seasonal patterns were not identified during this time of year.  As the continued collection of data progresses, results will become more comprehensive and should eventually show that water conservation methods are effective to decrease the building’s total water consumption. However, I would suggest that consistent monitoring of the building’s water meter during the summer months should allow for any observable patterns during this time of year to be identified.                       APPENDIX    0"2"4"6"8"10"12"14"13600)15199" 15200)16799" 16800)18399" 18400)19999" 20000)21599" 21600)23199" 23200)24799" 24800)26399"Frequency)Water)Consump2on)(43))Figure)1.)Monthly)Water)Consump2on)10000#12000#14000#16000#18000#20000#22000#24000#26000#28000#Water&Consump.on&(03 )&Date&Figure&2.&Time&Series&Replacement of old fixtures to water conserving fixtures Water Consumption 10000#12000#14000#16000#18000#20000#22000#24000#26000#28000#January# February# March# April# May# June# July# August# September# October# November# December#Water&Consump.on&(03 )&Month&Figure&3.&Yearly&Water&Consump.on&2010# 2011# 2012# 2013# 2014#Table&A1.&Unmodified&DataDateBase'Meter'Reading'(m3)Meter'Reading'(m3)September&17,&2014 25 75870August&19,&2014 25 75335July&17,&2014 25 74840June&17,&2014 25 74395May&21,&2014 15 73995April&22,&2014 15 73560March&14,&2014 15 72905February&20,&2014 15 72520January&17,&2014 15 71955December&17,&2013 15 71535November&11,&2013 15 71010October&10,&2013 15 70460September&18,&2013 15 69870August&20,&2013 15 69360July&19,&2013 15 68870June&14,&2013 None NoneMay&21,&2013 15 67850April&18,&2013 15 67335March&18,&2013 15 66780February&19,&2013 15 66315January&17,&2013 15 65715December&18,&2012 15 65290November&19,&2012 15 64745October&20,&2012 None NoneSeptember&13,&2012 15 63460August&17,&2012 15 62995July&18,&2012 15 62555June&18,&2012 None NoneMay&16,&2012 15 61525April&19,&2014 15 61115March&19,&2012 15 60525February&17,&2012 15 59990January&18,&2012 15 59330December&19,&2011 15 58870November&17,&2011 15 58250October&20,&2011 15 57640September&15,&2011 15 56915August&19,&2011 15 56400July&21,&2011 15 55930June&15,&2011 None NoneMay&18,&2011 15 54835April&19,&2011 15 54380March&16,&2011 15 53730February&16,&2011 15 53220January&19,&2011 15 52700December&16,&2010 15 52215November&17,&2010 15 51665October&20,&2010 15 51120September&15,&2010 15 50420August&18,&2010 15 49910July&19,&2010 15 49350June&16,&2010 15 48740May&19,&2010 15 48205April&20,&2010 15 47635March&16,&2010 15 46895February&17,&2010 15 46350January&19,&2010 None NoneDecember&16,&2009 15 45150Unmodified&DataTable&A2.&Modified&DataDateBase'Meter'Reading'(m3)Change'in'Base'Meter'ReadingMeter'Reading'(m3)Change'in'Base'Meter'Reading'(m3)Change'in'Water'Consumption'(m3)Change'in'Water'Consumption''(cft)September&17,&2014 25 0 75870 535 535 18893August&19,&2014 25 0 75335 495 495 17481July&17,&2014 25 0 74840 445 445 15715June&17,&2014 25 10 74395 400 410 14479May&21,&2014 15 0 73995 435 435 15362April&22,&2014 15 0 73560 655 655 23131March&14,&2014 15 0 72905 385 385 13596February&20,&2014 15 0 72520 565 565 19953January&17,&2014 15 0 71955 420 420 14832December&17,&2013 15 0 71535 525 525 18540November&11,&2013 15 0 71010 550 550 19423October&10,&2013 15 0 70460 590 590 20836September&18,&2013 15 0 69870 510 510 18010August&20,&2013 15 0 69360 490 490 17304July&19,&2013 15 K 68870 K K KJune&14,&2013 None K None K K KMay&21,&2013 15 0 67850 515 515 18187April&18,&2013 15 0 67335 555 555 19600March&18,&2013 15 0 66780 465 465 16421February&19,&2013 15 0 66315 600 600 21189January&17,&2013 15 0 65715 425 425 15009December&18,&2012 15 0 65290 545 545 19246November&19,&2012 15 K 64745 K K KOctober&20,&2012 None K None K K KSeptember&13,&2012 15 0 63460 465 465 16421August&17,&2012 15 0 62995 440 440 15538July&18,&2012 15 K 62555 K K KJune&18,&2012 None K None K K KMay&16,&2012 15 0 61525 410 410 14479April&19,&2014 15 0 61115 590 590 20836March&19,&2012 15 0 60525 535 535 18893February&17,&2012 15 0 59990 660 660 23308January&18,&2012 15 0 59330 460 460 16245December&19,&2011 15 0 58870 620 620 21895November&17,&2011 15 0 58250 610 610 21542October&20,&2011 15 0 57640 725 725 25603September&15,&2011 15 0 56915 515 515 18187August&19,&2011 15 0 56400 470 470 16598July&21,&2011 15 K 55930 K K KJune&15,&2011 None K None K K KMay&18,&2011 15 0 54835 455 455 16068April&19,&2011 15 0 54380 650 650 22954March&16,&2011 15 0 53730 510 510 18010February&16,&2011 15 0 53220 520 520 18364January&19,&2011 15 0 52700 485 485 17128December&16,&2010 15 0 52215 550 550 19423November&17,&2010 15 0 51665 545 545 19246October&20,&2010 15 0 51120 700 700 24720September&15,&2010 15 0 50420 510 510 18010August&18,&2010 15 0 49910 560 560 19776July&19,&2010 15 0 49350 610 610 21542June&16,&2010 15 0 48740 535 535 18893May&19,&2010 15 0 48205 570 570 20129April&20,&2010 15 0 47635 740 740 26133March&16,&2010 15 0 46895 545 545 19246February&17,&2010 15 K 46350 K K KJanuary&19,&2010 None K None K K KDecember&16,&2009 15 15 45150 45150 K KModified&Data

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