Over View Of Meter Data Analytics Manoj Kumar Gupta Neha consultancy services, Bangalore firstname.lastname@example.org
Meter Data AnalyticMeter Data Analytics refers to theanalysis of data emitted by electricSmart Meters that recordconsumption of electric energy.Smart meters send usage data to thecentral head end systems as often asevery minute from each meter whetherinstalled at a residential or acommercial or an industrial customer.
Analytical Methods Collecting accurate, timely and relevant data is the bedrock of any data analytics program, but the data needs to be put into an appropriate context to become useful information.1)Aggregations,2)correlations,3) Trending,4)Exception analysis,5) forecasting.
Analytical MethodsAggregationsAn aggregation is a summary of data using set criteria. Because smart meter data is is associated with a metering endpoint, it can be aggregated in dierent ways to serve planning purposes. For instance, the meters connected to individual transformers can be aggregated together to identify transformer loading patterns
Analytical MethodsCorrelations Correlations identify statistical relationships between related data that are useful for building predictions. A basic smart meter correlation is the relationship between outdoor air temperature and power consumption. The fact that heat waves causes spikes in power Consumption is well known fact . Statistical correlation using time-interval consumption data makes it possible to build algorithms that predict the size of demand spikes using forecast temperature.
Analytical MethodsTrendingTrending is one of the most basic forms of analytics, and it can be an obvious win for improving customer relations and service quality using smart meter data. A web page that shows customers a simple consumption data trend line can help them relate powerconsumption to household activity.
Analytical methodsException Analysis Exceptions are unexpected or abnormal conditions. A missing meter read, for instance, is an exception event. The ability to analyze exceptions over time is valuable for identifying problems in communications and measurement infrastructure, as well as in the distribution grid. Equipment failure is useful for homing in on a subset of data for other forms of analysis. In the case of a blown transformer, it may be useful to build a historical trend of transformer loading prior to the failure. Once pre-failure patterns are identified, they canbe used to build predictive algorithms useful for preventing future failures.
Analytical MethodsForecasts Forecasts are predictions of future events or values using historical data. For instance, a forecast of power consumption for a new residential subdivision can be created using historical data from similar homes. Forecasts can also be built using correlation data.
Analytics Application Meters data and analytics will revolutionize the way power is managed, delivered, andsoApplications:Revenue Management,Customer Engagement,Distribution optimization andAMI Network Management.
Challenges of Meter Data AnalyticsData required for complete meter data analyticssolution does not reside in the same database,instead, resides in disparate databases amongvarious departments of utility companies.Meter Data Analytics need to deal with big dataproblem.Many utility companies do not haveinfrastructure to support such needs
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