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.
Our investigations indicate that because accounting model descriptions of service providers lack clarity and completeness, collecting metering data is fraught with difficulties even for infrastructure level services that are conceptually quite simple.
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.
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.
One possible way of detecting a small crustal deformation is to use the stacking method (Miyaoka and Yokota 2012), in which multiple records of time-series strain-meter data are stacked to improve the SNR.
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.
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.
The incidence of hypoglycemia (<70 mg/dL), based on downloaded meter data, was 1.9% in the ACG and 1.8% in the STG (P = NS).
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.
