4. Lesson plan
4.2 Session 2: The adventures of Robin Hood
Although households had monitoring equipment installed from March 2014, this Phase 2 study focused on monitored data from the start of July 2014 until the end of April 2015, so to have the same start date for all twelve homes (removing the staggered installation of the new controls). Also by analysing data from July onwards it was possible to identify any households which were using their heating during the summer. For this doctoral research the shoulder months were taken to be August and September representing the autumn shoulder months and March and April representing the spring shoulder months. Therefore the winter months was taken to run from October to February. The selection of which months represented which season came from previous studies within the area, with October being monitored as part of winter analysis. However, the author
recognises there is a lack of clarity over exactly what months should be included in each season. The inclusion of both shoulder months seasons and winter analysis was done to uncover how the participant households used their heating in the lead up to winter and when they decided to stop or change their heating patterns after the main heating season, as well as how this differed to the heating use during winter. The data from monitoring the households coincided with the period when the households completed their heating diaries and when two out of the three
193 interviews were completed (the first interview being done pre-installation of the new controls).
Once the data were downloaded there were some gaps in the recordings within some of the homes. Figure 5.17 summarises the gaps in heating set-point data being sent to the online server. These gaps in data were caused by a number of unforeseen circumstances out with the control of the researcher. Sensors went offline occasionally as indicated by being greyed out on the server screen (as can be seen in Figure 5.7, Page 187), however it was during the second interview visit to households that it became apparent the reason for this was due to batteries within some sensors becoming loose, as the installer had not screwed the backing on tight enough. In the rare case of those offline sensors that had the backing secured properly, it was often the case that the sensor had been dropped which dislodged the batteries. These issues were easily solved during the second visit and the sensor reactivated on the server after the visit, but resulted in gaps within the data.
Other issues which caused data loss included households turning the gateway off and some households having weak wireless signal. Reminder emails and signal boosters were used with these households. Finally the last issue relating to data loss was caused by the online server itself being upgraded without any notice to the researcher.
Figure 5.17 Availability of heating set-point temperature monitoring data (white gaps indicating missing data)
194 There were particular households which had more data recording issues than others.
For example, shortly after the installation had occurred within P12, the system went offline which was likely due to the owner switching the gateway off. Then it came back online in September, however the owner the decided to rent out the property in October, after which the system went offline. So it is likely it was switched back on to get the property ready to rent and then once the new tenant moved in switched off again. P05 had issues with the signal within their home making it difficult for all sensors to be recorded, however there were further problems with the server meaning only a small amount of their set-point data were recorded. It was decided to accept this for this household so not to become a nuisance to the household as the system had already been completely rebuilt during the second interview visit, which took over 2 hours to complete. However, despite the rebuild, the sensors then went offline again a few weeks later so no further data were recorded.
A further issue was discovered during the second interview visits when trying to get sensors back online. It seemed the numbering on the room sensor did not
necessarily match the number of that sensor on the server, allocated by the installer. Although in most instances this numbering was only 1 out, there were a number of occasions where the numbering was completely random, where it was possible the installer paired all sensors to the server then randomly placed them into the rooms of the household. This meant that identifying the exact room each sensor on the server came from was not possible with complete certainty. Since the only way to discover which sensor was which was to un-pair them from the server individually it was accepted that although all rooms were monitored it would be too difficult to correctly identify each room, and therefore no analysis was carried out on individual rooms for comparison between the sample. However it was still possible to analyse a whole multi-room average temperature for each household for comparison, however this was limited to a simple average instead of a weighted average due to the room locations being unknown and therefore the individual space floor area being unknown.
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5.3 Data Analysis
An overview of the process behind creation of the data set and initial steps taken to clean and plot the data is presented. This is followed by details of the monitoring period and the analysis carried out on the specific areas where results are
presented from this study, such as set-point temperatures, internal temperatures, scheduled heating use, changes to default settings, manual use of controls, heating durations, placing this study into context and categorising heating use types.
5.3.1 Overview
A data set was created for each individual household within Microsoft Excel which contained the following data from the online server:
• set-point temperatures – these were the demanded set-point temperatures either from the Halo control default and scheduled settings or from manual interactions of the participants;
• individual room temperatures – these were the temperatures recorded by the individual wireless temperature sensors placed into all rooms in the household. These were not connected to the Halo controls only to the online server. There were up to ten individual wireless temperature sensors for each household, so the number of individual room temperature files varied depending on the total number of rooms in each of the sample households;
• thermostat temperature – this recorded the thermostat temperature sensor which was installed to replace the occupant’s existing thermostat;
• energy save heating – this refers to the baseline set-point temperature the heating control sets whenever left on AUTO, the default is 10oC therefore the boiler would fire outside of the heating schedule if the temperature dropped below this;
• away heating – this refers to the temperature set as the frost protection setting, therefore the heating controls refer to this setting whenever set to OFF or Holiday mode; and,
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• advance heating (manual interaction) – this records demanded set-points from any manual interaction with the controls outside of being left on AUTO.
The data set was created by compiling all the individual weekly .csv files which were downloaded from the server into MS Excel.
5.3.1.1 Cleaning the data set
Before any analysis of the data was started, the data sets first had to be cleaned to remove any errors or abnormalities. This data cleaning was done within Excel. The two main issues uncovered when cleaning the data were errors with the timestamp on the data and erroneous temperatures being reported.
As uncovered at the start of this study the reported time within the data file was one hour behind the correct time. To check that this was the same with the participating households the data were checked at the time of the second
household visit when the occupants were asked to perform various tasks with the controls outside this doctoral research. These tasks involved switching the heating on, changing schedules etc., all of which could be seen in the data file. Due to the second interview visit occurring in late summer the majority of households visited had their heating switched off at that time. By looking at what time the heating was switched on in the data file it was clear to see in six of the nine households visited that the time shown in the data file was one hour behind the manual interaction. In two of the remaining households the system had gone offline and was reactivated during the visit, however the time was still one hour behind. It was therefore assumed that the same issue was happening within all of the twelve households so the data was corrected via Excel by inserting a formula which added 1 hour to the original time.
During the nine months monitoring period the clocks changed twice: the end of British Summer Time (BST) in October and the beginning of BST again in March.
197 Due to this the clocks moved back an hour at 2am on the 26th of October 2014 and moved forward 1 hour at 1am on the 29th of March 2015. Upon checking the data downloaded from the server it was noticed that the clock changes were opposite to that expected. This meant that there was a 2 hour discrepancy between October 26th and the 29th of March. Therefore the time for this period was corrected in Excel using a formula.
The recorded temperature data were also checked for any erroneous readings. The maximum demand temperature possible with the new heating controls is 30ᴼC therefore anything above this within the following data files was removed:
• set-point temperature;
• advance temperature;
• energy save temperature; and,
• away temperature
Due to the demanded temperatures on the controls always being a whole number any readings within the data files identified above which had decimal values were also removed. The occurrence and number of these errors varied across the sample however only a small number of these errors were found.
However for the recorded room temperatures and thermostat temperature, it was more difficult to identify errors. This was due to these recordings potentially being influenced by various other factors such as secondary heating, direct sunshine and errors within the Halo controls and communication of recordings to the server.
Firstly any temperatures recorded which were above 100ᴼC were removed. This included a temperature error noticed in several data files, where a temperature of 3276.7ᴼC was recorded. Secondly the temperatures were listed in descending order therefore a note of all high temperatures could be made. These identified high temperatures were then investigated to eliminate any temperatures that were determined to be erroneous, an example being a recorded temperature was 46ᴼC
198 however both temperatures 5 minutes before and 5 minutes after were 10ᴼC. In instances such as these the inconsistent temperature recording was removed.
Where there was an apparent gradual increase in temperature, data were left within the data file as these were deemed to be temperatures gained during active heating periods. Plots were also made of the temperatures recorded for each household to see if there were any unusual peaks within these traces which were then investigated further, similar to when listing the temperatures in rank order.
Erroneous temperatures identified varied across the sample in relation to the total percentage of recorded data being identified as errors, however this ranged from 0%
(in 4 households) to 2% (P03).
5.3.1.2 Initial exploration of data set
The analysis of the data collected as part of this study can be described as both exploratory and confirmatory. Exploratory data analysis is used as the initial analysis as it explores the data to uncover what results the data shows. Initial exploration of the data set involved plotting the data in various formats and graphs to get a feeling for the data, in particular indoor temperatures, set-points and heating patterns. SPSS was used to produce summaries of the data sets in the form of mean air temperatures, level of variability and monthly comparisons. SPSS also enabled frequency distributions to be produced which quickly gave the frequency of occurrences of things such as the number of times a certain temperature was
demanded as the set-point or the frequency of manual interaction with the controls.
SPSS also allowed descriptive statistics to be produced on the data sets, often also referred to as summary statistics (Robson, 2011, p423) which can be used to represent the level of distribution and the spread within a sample. To measure the spread within a sample “measures of variability” are used which means that SPSS can calculate the following for a data set:
• standard error;
• standard deviation;
• range;
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• variance;
• mean deviation; and,
• inter-quartile range.
To measure the distribution within a sample “measures of central tendency” are used for which SPSS calculates the mean, median and mode for the data set. These measures of central tendency and variability were applied to the temperature data.
Using SPSS, the data could also be plotted to see whether it showed a normal distribution or not which was needed to determine which further statistical tests to carry out. If the data did not fit a normal distribution then non-parametric tests would be needed as these do not use the normal distribution shape assumption.
SPSS also allowed for correlation coefficients to be calculated for the heating data achieved and the sample descriptives to uncover any patterns or interesting findings. The correlation coefficients allow the strength and direction of a relationship between two variables to be tested to see how linear they correlate with one another.