forecasts vs. capacity, what-if analysis; Realtime information display; Apps with push notification of alerts Data Quality (syntax) Gaps in smart meters and weather data; Quality issues in sensor data; Rigorous checks done for meter data; “billing quality” Data Types Timeseries (CSV, SQL tuples), Static information (RDF, XML), topology (shape files) Data Analytics Forecasting models, machine learning models, time series analysis, clustering, motif detection, complex event processing, visual network analysis, Big Data Specific Scalable realtime analytics over large data streams Challenges (Gaps) Low-latency analytics for operational needs Federated analytics at utility and microgrid levels Robust time series analytics over millions of customer consumption data Customer behavior modeling, targeted curtailment requests Big Data Specific Apps for engaging with customers: Data collection from Challenges in Mobility customers/premises for behavior modeling, feature extraction; Notification of curtailment requests by utility/building managers; Suggestions on energy efficiency; Geo-localized display of energy footprint. Security & Privacy Personally identifiable customer data requires careful handling. Requirements Customer energy usage data can reveal behavior patterns. Anonymization of information. Data aggregation to avoid customer identification. Data sharing restrictions by federal and state energy regulators. Surveys by behavioral scientists may have IRB restrictions. Highlight issues for Realtime data-driven analytics for cyber physical systems generalizing this use case (e.g. for ref. architecture) More Information (URLs) Note: <additional comments>
forecasts vs. capacity, what-if analysis; Realtime information display; Apps with push notification of alerts Data Quality (syntax) Gaps in smart meters and weather data; Quality issues in sensor data; Rigorous checks done for “billing quality” meter data; Data Types Timeseries (CSV, SQL tuples), Static information (RDF, XML), topology (shape files) Data Analytics Forecasting models, machine learning models, time series analysis, clustering, motif detection, complex event processing, visual network analysis, Big Data Specific Challenges (Gaps) Scalable realtime analytics over large data streams Low-latency analytics for operational needs Federated analytics at utility and microgrid levels Robust time series analytics over millions of customer consumption data Customer behavior modeling, targeted curtailment requests Big Data Specific Challenges in Mobility Apps for engaging with customers: Data collection from customers/premises for behavior modeling, feature extraction; Notification of curtailment requests by utility/building managers; Suggestions on energy efficiency; Geo-localized display of energy footprint. Security & Privacy Requirements Personally identifiable customer data requires careful handling. Customer energy usage data can reveal behavior patterns. Anonymization of information. Data aggregation to avoid customer identification. Data sharing restrictions by federal and state energy regulators. Surveys by behavioral scientists may have IRB restrictions. Highlight issues for generalizing this use case (e.g. for ref. architecture) Realtime data-driven analytics for cyber physical systems More Information (URLs) Note: <additional comments>