As major Utilities begin to deploy Smart Meters and implement the Smart Grid the need to collect and exploit large volumes of energy consumption data and grid metrics becomes paramount. The IBM Netezza data warehouse appliance provides the perfect solution to their evolving data management challenge by combining simplicity of operation and implementation with the ability to scale and efficiently handle increasing complexity.
With IBM Netezza data warehouse appliance, Energy and Utilities providers and market players can increase operational efficiency, better comply with a growing number of government and industry regulations, respond more effectively to dynamic market changes and engage more actively with their consumers. IBM enterprise-ready appliance delivers outstanding high performance analytical capabilities from smart grid and smart meter deployments.
Capabilities
- Outage Planning
- Fraud Detection
- Wide Area Situational Awareness
- Condition Based Management
- Maintenance plan optimization
- Voltage Stability Monitoring
- Generation, Demand and Revenue Forecasting
- Optimum Rebate Design for Demand Response
- Virtual Power Plant Services
- Demand Response Services
- Building Energy Services
- Microgrid Services
- Electric Vehicle Services
- Intermittent Renewable Power Forecasting
- Improved asset and inventory management
- Smart meter analytics
- Smart grid analytics
- Customer behavior insight and profiling
- Demand response enablement
- Billing assurance
- Energy trading risk management
Customers
- Midwest ISO
IBM Netezza Data Warehouse Appliance Products
- IBM Netezza 100
An extremely versatile, compact, purpose-built, easy to install data warehouse appliance designed for test and development. - IBM Netezza 1000
Purpose-built, standards-based data warehouse appliance that architecturally integrates database, advanced analytics, server and storage into a single, easy-to-manage system that offers significant performance and scalability. - IBM Netezza High Capacity Appliance
Accelerating the industry’s leading massively-parallel data warehouse architecture to multi-petabyte scale, creating a “queryable archive” that can store, query and analyze thousands of terabytes of data quickly and cost-effectively.
