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New Bidgely Insights Engine Extends AI-Powered Disaggregation to Utility Decision Making

Enterprise SaaS Platform Optimizes Utility Programs, Identifies New Revenue Opportunities and Enhances Grid Services

Bidgely introduced the new Bidgely Insights Engine that extends the value of its artificial intelligence platform to a range of internal utility functions. The Bidgely Insights Engine analyzes meter data from both smart and monthly read meters, plus a variety of third-party data including weather, demographics and building data, to identify and segment customers by appliance-level ownership, usage and efficiency.

“Now, utilities are asking us to expand the value of our disaggregation platform to help with their internal decision-making. The Insights Engine complements our customer-facing AI solutions with enterprise-level business intelligence for program optimization, DERs visibility, load planning and beyond – a critical need as the grid rapidly evolves.”

Stakeholders from executives and analysts to program managers and program implementers can utilize this new business intelligence tool to optimize utility programs with targeted, personalized customer outreach and enhance grid modernization efforts like load forecasting and scenario planning for distributed energy resources (DERs).

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“Our core AI-powered disaggregation technology has been adopted by leading utilities and embraced by consumers, proving its value through modern customer engagement programs,” said Bidgely CEO Abhay Gupta. “Now, utilities are asking us to expand the value of our disaggregation platform to help with their internal decision-making. The Insights Engine complements our customer-facing AI solutions with enterprise-level business intelligence for program optimization, DERs visibility, load planning and beyond – a critical need as the grid rapidly evolves.”

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By analyzing homes based on appliance-level usage and other data, the Bidgely Insights Engine enables utilities to increase revenue; reduce cost to serve; optimize DSM program costs and enrollment; and increase customer satisfaction. Example use cases include:

  • DSM Planning and Program Optimization: Perform scenario analysis across all DSM programs to achieve highest adoption and lowest cost, such as how much to invest in pool pump rebate programs vs. an A/C tune-up program for a given population. Find and target homes with large and inefficient appliances that would offer the most savings per rebate, in addition to which appliances run at peak hours for Demand Response, optimizing program spend to achieve savings goals.
  • Rate planning: Understand the impact of introducing various rates on customers’ bills. Includes solar disaggregation that can identify how much energy a home produces and consumes hour-by-hour without sub-metering, savings millions of dollars.
  • Electrification Analysis: Find homes that have Electric Vehicles with L2 or L3 chargers and determine how often they charge during peak and off-peak periods, helping to inform EV planning and encourage customers to sign up for EV Time-of-Use rates.

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