Utilities

Transform business in many ways from smarter demand management to better weather predictions to finding new energy sources.

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Why AI Now

Consumers often don’t give their power a second thought — until it goes out. Although they may not realize it, the systems and processes preventing that from happening are getting smarter every day. Utilities that embrace the potential of machine learning and AI will create a less expensive and more reliable service for their customers. Those that don’t will find themselves going the way of the incandescent light bulb. From smarter demand management to better predictions of weather and other disruptions to finding altogether new energy sources and markets, utility companies have myriad ways in front of them to transform their businesses. RapidMiner can help utilities tap into data science to ensure smooth delivery, profitable operation and great customer experiences.

Utilities Industry Use Cases

Mobirise

Drive Revenue

1. Forecast demand and predict         which incentives and tariffs will     improve load management

2. Develop customer insights to          improve targeting of                          campaigns about useful                    programs and offers

Mobirise

Cut Costs

1. Predict failure of field and plant     equipment and schedule                 maintenance optimally

2. Increase accuracy of storm             impact to anticipate and                   prepare for outages

3. Accurately predict production       and consumption to avoid               costly purchase of peak energy     on short notice

Mobirise

Avoid Risks

1. Protect customers and escape       liability by identifying signs of         fraud quickly and stopping it

2. Profile EH&S risks and take              proactive measures to protect        people and the environment

Business Value

A utility used machine learning to improve its demand forecasting and ensure it had available capacity.

A power generator protected its revenue by identifying patterns indicating theft was occurring and taking action.

A utility increased revenue by identifying potential underreporting of consumption from incorrectly configured meters and fixing them quickly.

A customer service department used predictive analytics to make better forecasts of incoming call volume and optimized live agent staffing.

A maintenance division predicted which mission critical transmission equipment would need repair next, and planned the lowest-cost repair measure.

A utility conducted root cause analysis of power outages and took preventive steps, reducing outage frequency and duration and increasing customer satisfaction.

A customer service department analyzed outcomes and ensured its call centers were following best practices.

A billing department reduced non-payment of bills by predicting likelihood of delinquency and undertook appropriate interdiction and resolution.

A security team identified the signs of suspicious logins and improved cyber defense of its power network.

Let's talk about the ways data science can help your organization drive revenue, cut costs, and avoid risks.

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