6s097Class 2 : Urban Solutions - Energy Use cases for

machine learning

Challenges facing energy and utilities providers

Increasing desire by consumers for a role in energy management and conservation

Aging asset performance with increased expectations on

reliability

New entrants and disruptive technologies

Growth in renewable generation and distributed resources

Increased pressure on operational efficiency and workforce productivity

Climate change and environmental concerns

Distribution and operations … could predict outages and track demand patterns based on past history of weather, loads and environmental factors?

Executive team … could make better business decisions using accurate data across all time horizons: past, present and future?

Finance … could reduce electricity theft and fraud with smart meter data?

Network planning … could reduce the number of truck rolls to confirm service after outages?

Various opportunities for analytics and machine learning

Customer service … could offer personalized guidance on energy usage patterns and incentives to reduce consumption?

Demand management … could offer time-based pricing based on online energy audits?

7

Improve Generation Performance

- Align organization and processes to deliver the right products and solutions to each customer - Enable more efficient customer sales and service

interactions - Minimize fraud

Transform the Utility Network

- Improve generation efficiency and reduce operating expenses - Maximize power generation uptime through

predictive maintenance

- Reduce outages and downtime - Optimize maintenance and operational activities - Time of use pricing flexibility - Comply with information privacy and retention

regulations

Smarter Analytics for Energy and Utilities

Industry Imperative Machine Learning Outcome

Transform Customer Operations