Boost Energy Efficiency via Predictive Analytics

An increasing awareness of global warming combined with new economic challenges are driving companies to re-evaluate existing processes to find ways to become more energy efficient. Companies in the energy sector have to balance a multitude of energy demands while maintaining their infrastructure and attempting to develop renewable energy. Heinrich Group’s machine learning software can give you better decision making capabilities by showing you energy consumption patterns from existing datasets and making predictions on the future of energy.

Designed by top researchers, Heinrich Group’s machine learning software can produce accurate, scalable, and predictive models that can be up to 10,000 times faster than current alternatives.

Heinrich Groups algorithms can help you:

Increase operational efficiency

  • Predict peak seasons. Find patterns in energy consumption to efficiently reallocate resources to anticipate spikes in activity during different times of the year
  • Respond faster. Keep updated with the latest analysis and quickly react to changes in behavior to minimize risk
  • Boost issue detection. Spot outliers and changes in energy consumption early on to continuously implement innovative measures

Focus on Business Results

  • Data based decisions. Conduct cost-benefit analysis on different energy alternatives and energy distribution frameworks to create the most efficient combination of options
  • Reduce waste. Identify excessive uses of resources to allow you to improve resource allocation and save substantial amounts for your organization
  • Grow existing offerings and develop new ones. Identify key energy programs that drive revenue and are most eco-friendly to increase targeted research and development

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