Development of an IoT System for the Generation of a Database of Residential Water End-Use Consumption Time Series †
2. Materials and Methods
- Understanding the average and peak end-use water consumption volumes of different fixtures (shower, toilet, etc.) at hourly, daily, and monthly levels of resolution to improve planning processes;
- Evaluating daily water end-use patterns to identify trends and peaks in water consumption throughout time;
- Providing updated information on demand per capita that are not evaluable with traditional methods, which do not take into account social changes over time;
- Examining peak day demand to understand the types of household practices that drive peak usage; and
- Evaluating the seasonal impact of water usage.
2.1. IoT Monitoring System Architecture
2.2. Case Study Setup
2.3. Data Collection
- Figure 3 shows the water end-use consumption and the duration that characterize usage events during day hours, which is evaluated across the entire period of observation. The first analysis of data allowed estimating the mean and the standard deviation of water consumption and duration of use per fixtures evaluated on all the events detected during 8 months of monitoring, as shown in Table 1:
- Figure 4 shows the distribution of probability related to the use of a fixture during weekdays. Specifically, in the case study, the distribution of probability expresses how often the event occurs on a particular day (that is, the ratio between the numbers of events that occurred on that day and the total number of events). In this case, the kitchen was illustrated. Figure 4a represents the average distribution of probability evaluated in the period of observation. It is relevant to observe in Figure 4b a significant change of such distribution relating to the user’s habits each month. It is obviously affected by climate/seasonal variability, different timings of use due to changes in personal practices, etc. To remark on this aspect, Figure 4c,d shows the variation of the distribution during April and August. It can be observed that the density is higher during the week beginning in April and moving toward the weekend in August.
- Figure 5 shows the number of uses during day hours for different fixtures evaluated on the entire dataset of consumption. The results highlight how uses are concentrated between 07:00 and 10:00 in the morning and after 20:00 in the evening, reflecting the profile of the user who is an employee and is generally at work during the day. Moreover, also in this case, the evaluation of the same trend within a single month allows the deepest knowledge of the user’s behaviors variability. In fact, using a kitchen faucet as an example, Figure 6 illustrates the changeability of the trend, taking into account the number of uses during April and August.
- Figure 6 indicates how habits clearly affect the use of water in a domestic environment. The figure represents the number of uses during day hours in a working month (April) and in a non-working month (August), revealing a more frequent use during the day hours in the working month, August.
Conflicts of Interest
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|Water consumption per event [l]||4.5/3.5||6.3/4.9||21.2/13.4||4.4/3.9|
|Duration per event [m]||6.4/2.2||9.4/4.0||9.8/3.3||8.7/3.5|
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Mauro, A.D.; Nardo, A.D.; Santonastaso, G.F.; Venticinque, S. Development of an IoT System for the Generation of a Database of Residential Water End-Use Consumption Time Series. Environ. Sci. Proc. 2020, 2, 20. https://doi.org/10.3390/environsciproc2020002020
Mauro AD, Nardo AD, Santonastaso GF, Venticinque S. Development of an IoT System for the Generation of a Database of Residential Water End-Use Consumption Time Series. Environmental Sciences Proceedings. 2020; 2(1):20. https://doi.org/10.3390/environsciproc2020002020Chicago/Turabian Style
Mauro, Anna Di, Armando Di Nardo, Giovanni Francesco Santonastaso, and Salvatore Venticinque. 2020. "Development of an IoT System for the Generation of a Database of Residential Water End-Use Consumption Time Series" Environmental Sciences Proceedings 2, no. 1: 20. https://doi.org/10.3390/environsciproc2020002020