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Analytics Case Study Series: 2. Energy & Utilities

The Utility industry can be brought to the internet age through Automated Meter Infrastructure (AMI) and Smart Grid. Enabling direct communication with metering devices opens up a realm of possibilities for understanding usage patterns that were not even registered. The ability to measure and analyze data about energy distribution and consumption on a more granular basis—in time and in detail—can unlock significant value.

Key Application Areas:

  • Demand response
  • Revenue management
  • Fraud and loss prevention
  • Energy efficiency
  • Compliance
  • Asset maintenance and management
  • Customer care and management
  • Forecasting and load management

The Case Details:

Organisation: Business Brio

Client: An energy company

Vertical: Wind Energy

Geography: The Netherlands

Category: Predictive

The Situation:

  • Challenges in accurate prediction of wind energy generation at a particular point of time, due to very stochastic nature of wind energy generation
  • This prediction was critical to save conventional energy due to 8/12 hour lag in ramp-up/down of conventional plants

The Solution:

•Regression and time series method to predict wind energy generation in shorter time horizon (<= 8 hrs)

•Artificial neural network and ensemble with (ordinary linear regression) to forecast for medium time horizon (>8 and <24 hrs)

Results:

Forecasted wind energy supply in a power grid for understanding the need of conventional energy as

  controlled input with 84% and 76% accuracy in cases of short term and medium term prediction respectively. 

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