Variety The flux data is relatively uniform, however, the (multiple datasets, biological, disturbance, and other ancillary data needed mashup) to process and to interpret the data is extensive and varies widely. Merging this data with the flux data is challenging in today’s systems. Variability (rate of change) Big Data Science Veracity (Robustness Each site has unique measurement and data processing (collection, curation, Issues) and Quality techniques. The network brings this data together and analysis, performs a common processing, gap-filling, and quality action) assessment. Thousands of users Visualization Graphs and 3D surfaces are used to visualize the data. Data Types Described in “Variety” above. Data Analytics Data mining, data quality assessment, cross-correlation across datasets, data assimilation, data interpolation, statistics, quality assessment, data fusion, etc. Big Data Specific Translation across diverse datasets that cross domains and scales. Challenges (Gaps) Big Data Specific Field experiment data taking would be improved by access to existing data and Challenges in Mobility automated entry of new data via mobile devices. Security and Privacy Requirements Highlight issues for generalizing this use case (e.g. for ref. architecture) More Information (URLs) Energy> Use Case 51: Consumption Forecasting in Smart Grids Use Case Title Consumption forecasting in Smart Grids Vertical (area) Energy Informatics Author/Company/Email Yogesh Simmhan, University of Southern California, Actors/Stakeholders Electric Utilities, Campus MicroGrids, Building Managers, Power Consumers, Energy and their roles and Markets responsibilities Goals Develop scalable and accurate forecasting models to predict the energy consumption (kWh) within the utility service area under different spatial and temporal granularities to help improve grid reliability and efficiency.
Variety (multiple datasets, mashup) The flux data is relatively uniform, however, the biological, disturbance, and other ancillary data needed to process and to interpret the data is extensive and varies widely. Merging this data with the flux data is challenging in today’s systems. Variability (rate of change) Big Data Science (collection, curation, analysis, action) Veracity (Robustness Issues) and Quality Each site has unique measurement and data processing techniques. The network brings this data together and performs a common processing, gap-filling, and quality assessment. Thousands of users Visualization Graphs and 3D surfaces are used to visualize the data. Data Types Described in “Variety” above. Data Analytics Data mining, data quality assessment, cross-correlation across datasets, data assimilation, data interpolation, statistics, quality assessment, data fusion, etc. Big Data Specific Challenges (Gaps) Translation across diverse datasets that cross domains and scales. Big Data Specific Challenges in Mobility Field experiment data taking would be improved by access to existing data and automated entry of new data via mobile devices. Security and Privacy Requirements Highlight issues for generalizing this use case (e.g. for ref. architecture) More Information (URLs) Energy> Use Case 51: Consumption Forecasting in Smart Grids Use Case Title Consumption forecasting in Smart Grids Vertical (area) Energy Informatics Author/Company/Email Yogesh Simmhan, University of Southern California, Actors/Stakeholders and their roles and responsibilities Electric Utilities, Campus MicroGrids, Building Managers, Power Consumers, Energy Markets Goals Develop scalable and accurate forecasting models to predict the energy consumption (kWh) within the utility service area under different spatial and temporal granularities to help improve grid reliability and efficiency.