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Washing machine and dryer use at campus Leidse Schans Mio T.L. Poortvliet December 4, 2019 Resident Committee Leidse Schans Abstract user will often have more laundry than can be done with one machine. The laundry room is often too The resident committee (RC) of campus Leidse busy in the experience of users, as is backed up by Schans collected data about washing machine and above calculation. Finding a time slot to do laundry dryer use between October 27 and November 18. can be a challenge. From this data conclusions can be drawn about peak The resident committee looked at laundry room use and careful guesses can be made towards the in- use. Data was collected of washing machine and fluence of temperature and rain on the use of the dryer use during a period between October 27 and laundry room. It should be noted that the infor- November 18. The feedback from residents led to mation from duwo.multiposs.nl, where machine use the question: ‘are there enough machines for all res- was recorded from, does not report accurately. The idents to do their laundry?’ To answer that ques- laundry room is used least during 02:00-06:00 and a tion properly another one needs to be answered first: smaller dip in use is observed between 15:00-18:00. ‘what are the influences on laundry room usage?’ In Rain and temperature seem to have no effect on this document the results are shown, interpreted and laundry room use, but more data is needed to attenu- based on them a conclusion is formed. ate statistical fluctuations and seasonal dependence. Two solutions are proposed to increase capacity and In the following document the analysis of the RC user friendliness of the laundry facilities. Currently is presented. Sections 2 and 3 explain and justify the demand outweighs supply. way data is presented in section 4 and further. The reader can skip sections 2 and 3, they can always come back to them if they are interested. 1 Introduction Campus Yours, or campus Leidse Schans, is a resi- dential project that is nearing its completion. At the time of writing the campus provides 1493 houses [1] , 2 Gathering data 1070 of which belong to student housing cooperation DUWO [2;3;4] . The DUWO buildings have the oppor- Bart Bussink set up a web scraper that gath- tunity to use the laundry room, this is a room on the ers information from the online laundry portal ground floor of Omegaplantsoen that contains eight duwo.multiposs.nl. This website displays the avail- washing machines and five dryers. That comes out able amount of washing machines and dryers in real to one washing machine per 134 houses. Assuming time. However, there is a fluctuating discrepancy every house uses one washing machine and all ma- between actual machine use and the reports from chines are used all day, each house gets 1 hour and 15 duwo.multiposs.nl which is discussed in depth in sec- minutes of machine time. However, this calculation tion 5. This data, along with date and time of is based on a flawed assumptions; it is not realistic recording were logged in a database. Besides this to expect users to do their laundry in the middle of data the temperature and probability of rain were the night. Another flaw in that calculation is that a recorded. 1 3 Methods of analysis 3.2 Length of use The cross correlation function of washing machine The gathered data is analyzed using python and and dryer use is calculated and plotted directly from numeric, scientific and plotting modules. Vari- the original data set. Figure 4 contains the resulting ous plots are made to show trends in the use of plot. washing machine and dryer use. The goal is to gain insight into the behaviour of laundry room use. The data and code are publicly available at 3.3 Rain and temperature dependence https://github.com/MioPoortvliet/Wasmachines- The average available machines at a temperature Leidsche-Schans. and chance of rain are plotted in figure 5. The coloured surfaces represent the standard deviation in the data set. In the background the histogram of 3.1 Time dependence the rain respectively temperature is shown to show One of the most important factors in machine use is how much data is available. Presenting the data in time. To gain insight the machine use is compared this manner, it is easy to see what is going on. How- through various slices of the data set in time. ever, information is lost. 3.1.1 Use throughout the week 4 Results First, the data is sorted by day and the mean num- Machine use depends on a lot of variables. Some are ber of machines available at a specific day of the easy to measure such as the time of day, day of the week at a specific time are calculated. Corrupt en- week and weather. The analyzed data can now be tries are removed. This plot considers machine use examined visually and conclusions may be drawn. throughout the days of the week. The average ma- chines available on a day is shown as a function of 4.1 Time dependence the day, see figure 1. Time is an important factor in machine use. The mean use throughout the week is considered and af- 3.1.2 Use throughout the day ter that, the mean use throughout the day. Another plot considers use throughout the day. The 4.1.1 Use throughout the week mean use in a time interval of 45 minutes over all days of the week is calculated. To correct for the First the dependence on the day of the week needs varying times of recording and to somewhat smooth to be considered. Figure 1 depicts the average use out the data an interval of 45 minutes is chosen. This throughout the week. The use seems to have a max- is plotted in figure 2. imum on Wednesday, although within one standard deviation. The weekend does not have a lot of im- pact. 3.1.3 Rate of change A possible explanation for the shape of the curve is that there is relatively little data for Wednesday. With this processing the rate of change can also be The fluctuations inherent in the duwo.multiposs.nl computed, this is plotted in figure 3. This is sim- reports are more pronounced due to this. ply the differential quotient of the machines over the time. This signal is further filtered to smooth it. 4.1.2 Use throughout the day To do so, the moving point average of n points is taken. Note that as ∆t becomes greater, the differen- Since the dependence on day of the week is relatively tial quotient grows smaller because the fluctuations small, it is justified to take the sum of all those days. are on a relatively small timescale. Other plots were made similar to figure 2 where each 2 Figure 1: The average use of the machines on the vertical axis is shown. The horizontal axis represents Figure 2: The total mean machine use during the a certain day of the week. The standard deviation is day. Washing machines are plotted in blue and dry- represented by the colored surface. The curve does ers in orange. The time of day is represented on not look flat, although the minimum and maximum the horizontal axis while the average available ma- are within one standard deviation of each other. chines are represented on the vertical axis. The use decreases at 21:00 until 02:00 when it reaches a min- imum. Between 06:00 and 09:00 the use picks up day was plotted individually. This way of present- drastically and stays roughly constant during the ing the data provides no further insight, however the day, with a small dip just before 18:00. After 21:00 shape of the curves match up to what can be seen in the use slowly decreases back to its minimum. figure 2. This further justifies the averaging through- out the days of the week. Figure 2 shows the average use of the machines in time. Noticeably, it is very rare that all machines are available to use. The peak moment starts from 06:00 and continues until 16:00 as can be read di- rectly from figure 2. From 16:00 to 18:00 there is a slight bump in machine availability after which it decreases again. From 20:00 the availability slowly builds up again to 01:00. Noticeable is that on av- erage about two machines are always in use, even between 01:00 and 06:00. This is due to incorrect reports from duwo.multiposs.nl as is discussed in sec- tion 5. 4.1.3 Rate of change Figure 3: On the horizontal axis the time is repre- sented while on the vertical axis the rate of change Figure 3 depicts the rate of change in machine use of the machine use is represented. At the start of over time. A smaller time averaging was chosen to the series the rate of change is negative. It stays get a better look at the volatility of machine use. around zero from 04:00-08:00 after which it becomes The physical interpretation of this plot is that a pos- positive. Between 11:00 and 19:00 oscillations can itive rate of change corresponds to people using the be seen. After 19:00 the rate of change abruptly washing machines. A negative rate of change cor- decreases, peaking sharply around 23:00. It slowly responds to the end of washing program, implying trends back to zero. someone will come pick up their laundry. Thus it 3 the plot, with a periodicity of 24 hours. This is due to the periodicity of the washing machine use from day to day, further confirming that the x-axis can be interpreted as time with units of hours. 4.3 Rain and temperature dependence To see if the laundry room use is influenced by the rain or temperature they were also recorded. Fig- ure 5a shows the average machines used dependant on temperature. As can be seen from the figure, no statistically significant temperature dependence is present. Around 00 C a bump is seen. Although Figure 4: The cross correlation between the washing this implies that at a colder temperature less peo- machine and dryer use is plotted in a range between ple do laundry, this can be attributed to the lack of 0 to 3 hours. The horizontal axis represents time, data in this regime. The colored surface signifying approximately. The vertical axis is the normalized one standard deviation agrees with this hypothesis. correlation function. Its maximum is at exactly 1:00 If the events are correlated, they are not necessar- hours, which is accentuated by an orange dot. ily caused by one another; during the period when less people do their laundry, at night, it is colder. An important thing to remember is that this data is may be concluded that machine use increases from gathered in the transitional period to cold weather. 08:00 to 11:00 after which the use peaks. A positive By gathering data over a longer time period seasonal rate of change signifies the machine use increasing fluctuations will be averaged out. and a negative rate of change signifies a decrease of From figure 5b no direct correlation between rain machine use. Between 11:00 and 19:00 oscillations and laundry room use can be observed. with a period of roughly an hour are observed, this is due to the average time a cycle on the washing machine takes. 5 Discussion 4.2 Length of washing machine use First, we would like to note that the figures gath- ered from duwo.multiposs.nl are not accurate and Looking at figure 2 it is noticed that the dryer use histograms of washing machine and dryer use should lags behind the washing machine use. The time it be taken with a grain of salt. Some machines lags behind can be approximated using the cross are unlocked and may be used without paying. correlation between the washing machine and dryer These machines always show up as unavailable in data series. This cross correlation function is cal- duwo.multiposs.nl. For some reason, sometimes the culated and plotted in figure 4 in the range of 0 online laundry portal reports 2 washing machines to 36 samples, which approximately corresponds to available when they are actually all in use. Further- three hours. The polling rate of the web scraper was more, we would like to point out that in the data five minutes, thus every sample translates to roughly there are times where the laundry facilities are not 5{60 hours. This is not exact since the web scraper used as much. These periods are during the night sometimes failed to record the data properly, how- (23:00-08:00). It is unreasonable to expect that res- ever it is close enough to give a good indication. idents do their laundry during this time. Therefore the x-axis can be interpreted as a time with units of hours. At around one hour the corre- 5.1 Criticism of the current system lation peaks, this can be interpreted as the average time the washing machines are used. In this correla- As it stands, doing laundry with the machines and tion function other bumps are noticed, not shown in system available is not as user friendly as it could 4 be. The website duwo.multiposs.nl is slow to load, reports machine use inaccurately and the reservation system is fundamentally flawed. Reservations have no meaning since they are only valid for 15 minutes. A reservation may be made every hour on the hour. To illustrate the flaw with an example, say a reservation is made for some hour. Then, 15 minutes before the start of that hour all washing machines start a new cycle. The mean of a cycle is one hour, therefore when the reservation is valid all machines are still in use and laundry can not be done. Besides that, the machines sometimes ‘swallow your money’; after you pay in the duwo.multiposs.nl system they do not unlock. On the other hand there are machines that are always unlocked, meaning you (a) The machine use appears to be correlated with the do not have to pay to use them. temperature. At lower temperatures the machine use de- Another criticism of the laundry facility is that creases, however this is within a standard deviation. Pos- residents have reported dirty machines and often one sible explanations are presented in section 4.3. or more machines are not operational or do not work as intended. Our own observations line up with these reports, the machines do not seem to be maintained as well as they could be. 5.2 Unknowns in the analysis Time and weather are not the only influences on laundry room use. There are unknowns and influ- ences we cannot quantify. One such factor is that there are residents who do not use the laundry ma- chines on campus because of the problems stated above, but would like to if doing laundry with them (b) The machine use does not seem to depend on the was a less painful process. The above analysis can rain. It fluctuates somewhat, however these fluctuations not take this into account, meaning that if these res- are well within a standard deviation. idents were to use the laundry room the presented plots would have different characteristics. Figure 5: In the background, a histogram of the count of data points are shown. It is not scaled 5.3 Proposed solutions to match the axis. The lines represent the average machine use dependant on the temperature respec- The laundry facilities in their current state can not tively rain. The colored surfaces once again repre- keep up with demand. An obvious solution that will sent one standard deviation. The red line represents solve the problem is to place another laundry room the washing machine use and the blue line the dryer in the available room in Sigmaplantsoen. This is use. something that has been proposed before. DUWO has informed residents that this is not an easy oper- ation due to external constraints. The transparency from DUWO is appreciated. Another solution could be better maintenance of 5 the laundry room and a new reservation system. If References all machines are operational at all times and work as intended they could keep up with demand more [1] “YOURS - Te huur: 423 apparte- than they do now. In the new system users have menten in Leiden.” [Online]. Available: to make a reservation to use the laundry facilities. https://leidenisyours.nl/ Users will have to pay upfront to make a reservation. [2] “Nog eens 470 studentenwoningen op cam- Then they will be guaranteed the opportunity to use pus Leidse Schans.” [Online]. Available: the machines. However, this system relies on the https://www.duwo.nl/over-duwo/duwo-nieuws/ fact that all machines are operational and work as het-laatste-nieuws/nieuwsbericht/item/204/ intended at all times. If they do not, users will be charged money without getting to do their laundry. [3] “Campus Yours (Epilsonplantsoen.” [On- A criticism of this solution is that it is less efficient line]. Available: https://www.duwo.nl/ than the current system since there is a low duty over-duwo/onze-gebouwen/projectinformatie/ cycle due to the reservation system. It might not projectdetails/item/31/ increase the capacity of the laundry facilities, it does makes the process of doing laundry less frustrating. [4] “Sleuteluitgifte op Leidse Schans.” [Online]. Available: https://www.duwo.nl/over-duwo/ duwo-nieuws/het-laatste-nieuws/nieuwsbericht/ 5.4 Conclusion item/207/ Currently the laundry system on campus Leidse Schans is in poor condition. To residents it feels like there are not enough machines to keep up with demand while the data tells a different story. This is due to unfaithful reports from duwo.multiposs.nl. There is small potential for more efficient use of the laundry room and demand outweighs supply by more than that. In the future, a model can be made to predict laundry use from the gathered data. This can optimize laundry room use. Corresponding Author To contact the author of this report please send an email to [email protected]. Conflict of Interest The RC is subsidised by BRES, which is an organ of DUWO. The author is a resident of Campus Leidse Schans. Acknowledgements The author would like to thank Bart Bussink for set- ting up and running the webscraper. Bart initiated this research. 6