Maximize Efficiency for Utilities Demand Forecasting
A natural gas and electricity utilities provider that serves nearly 4 million customers and invests over $1 billion in capital each year to improve service.
The company was struggling to accurately anticipate how much demand different areas of their network would have. This led to supply problems and scrambles to fill service gaps. They needed a predictive modeling platform that could automate processes and maximize efficiency by allowing accurate, proactive supply adjustment.
The client’s leadership team brought Ironside on as an expert resource to help with the following:
Discover Data Resources
Assess existing data quality and refine it for use in new predictive models
Run prepared data against regression and time series analysis algorithms
Implement a single platform for predictive analytics using SPSS Modeler and C&DS
Connect demand forecasts to the company’s energy management system
Prepare the Data
Leverage skills of analysts and Ironside experts to clean, transform and impute demand data from over 40 different geographic points of delivery
Enrich the core facts with geographic and weather data to account for the factors affecting utilities usage in each area
Develop & Test the Model
Refine and consolidate data from all points of delivery
Migrate to a more extensible and flexible predictive analytics solution
Test multiple predictive modeling approaches to find the most relevant
Reduce the need for manual rework of results through standard workflows
Automate connections between predictive analytics tools and other analytics assets/operational systems
Mentor internal resources to show future possibilities for predictive analytics and build comfort with the platform
Deploy to the Organization
Integrate the projections with their BI platform to compare against actuals
Use each point of delivery’s forecasts to trigger automated alerts in the energy management system upon generation
Allow proactive energy supply adjustments in different geographies based on predictive trends
KEYS TO SUCCESS
The demand forecasting project enabled the client to:
Increase data richness to generate more accurate and flexible predictive modeling results
Boost visibility and actionability of demand forecasts through energy management system integration
Empower internal analyst resources through a formal platform and hands-on mentorship
Implement a flexible SPSS predictive analytics platform able to extend to additional use cases
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