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Identifying relationships between air temperature and domestic water consumption using smart metering Maria Xenochristou Prof. Zoran Kapelan Prof. Slobodan Djordjevic Prof. Jan Hofman
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Case study Wessex Water: 2,000 properties since 2014
Smart demand metering data at half-hourly intervals Household characteristics Socio-economic data Met office / NOAA: Air temperature Precipitation Relative humidity garden size, rateable value, and council tax band, postcode, occupancy rate, metering status ACORN, groups and types National oceanic and atmospheric administration
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Results: Relationships between water consumption and the weather
Methodology: systematic approach, i.e. varying different aggregations of properties, based on household characteristics and socio-economic data. Statistically significant Correlation Stronger Correlation for: Certain times in the day (mornings and evenings) when water is typically used for gardening Certain types of properties (properties on a meter with gardens). Weekdays, metered, gardens
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Results: Relationships between water consumption and the weather
Methodology: systematic approach, i.e. varying different aggregations of properties, based on household characteristics and socio-economic data. Statistically significant Correlation Stronger Correlation for: Certain times in the day (mornings and evenings) when water is typically used for gardening Certain types of properties (properties on a meter with gardens). Weekdays, metered, gardens
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Conclusions Future Work Correlation increases for Test additional
Working days Evenings Summer & Spring Large & Medium Gardens High & Medium Rateable Value Affluent residents Medium occupancy Metered Properties Further aggregation of data For example: ACORN, council tax band, rateable value Observe at what level the inherent randomness of water use that you would identify for an individual HH disappears Decision tree classifier that determines which are the most influencing factors Weather variables Temporal & Spatial scales Data Mining techniques
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