The level of encryption we use to secure smart meter data is defined by the Department of Energy and Climate Change and is even more sophisticated than that used in internet banking.
Smart meter data are typically bundled with social economic data in analytics, such as meter geographic locations, weather conditions and user information, which makes the data sets very sizable and the analytics complex.
All total seed mass per square meter data were square root transformed to improve homoscedasticity, and error bars in the figures were back-transformed.
Therefore, patients' blood glucose meter data were included for analysis only if the patient's meter had a storage capacity of ≥28 days.
We examined whether the 72 children whose data were used differed from the 36 children from the HANDling Diabetes project whose blood glucose meter data were insufficient for inclusion; no significant differences in A1C, child sex, age, or disease duration emerged.
CGM data were obtained at 5-min intervals over a period of 5 days, and at the end of the 5-day sessions, the sensor and meter data were downloaded on the dedicated clinical center computer and used to calculate measures of GV as described (14).
In another study of 85 adults with type 1 diabetes, Kovatchev et al. (18) reported that LBGI values from home glucose meter data were significantly higher in the 24 h prior to and immediately following an SH episode compared with other days in the same subjects.
Pritchard et al. [2] provided an illustrative example of this potential: a large set of energy metering data was combined with train timetables and geographical (GIS) databases of network topology in order to compare the energy consumption of fleets of trains on different routes.
Metering data are costly to achieve, and in this paper we study two methods for end-use estimation, which can be applied on household data for appliance holdings, demographic and economic variables.
The data are used to conduct an in-sample validation of the modeled activities and a rigorous statistical validation of the predicted electricity demand against metered data is provided.
