From Data Chaos to Grid Clarity: GIS Load Forecasting for Distribution System Planning at Scale

How 9+ Utilities Used LoadSEER to Pinpoint Load Hotspots and Plan Upgrades Ahead of Electrification

SUMMARY

Utilities can’t modernize what they can’t see. Tamazari led project management for a multi-utility implementation program of LoadSEER, a spatial load forecasting platform from Integral Analytics. This tool helps planners predict where electric demand will grow and by how much. Across 9+ major utilities, we converted complex data environments into actionable, map-based forecasting insights. These insights improve grid planning, capital prioritization, and capacity decisions.

CLIENT BACKGROUND

This implementation program supported a diverse set of U.S. utilities—municipal, public power, investor-owned, and holding company structures. Each utility had different planning rhythms, governance models, and data maturity. Our clients included major players like Seattle City Light, Xcel Energy, OPPD, PG&E, SDG&E, SMUD, NV Energy, and Exelon. The common thread? All needed more precise geographic intelligence to plan distribution investments. Load growth is becoming less predictable and more location-specific.

THE CHALLENGE

Spatial load forecasting sounds straightforward until you encounter real utility data. LoadSEER normalizes to kWh. However, many utilities track and store load-related data in different units and structures. This includes amperage, wattage, and varying historical schemas, often across multiple source systems. Large implementations involve multiple vendors and internal teams, each with their own tools, reporting norms, and expectations. As a result, technical delivery can turn into a coordination bottleneck.

SUCCESS CRITERIA

To ensure a successful implementation, we established clear success criteria:

• Implement LoadSEER across diverse utility environments without overburdening internal technical staff.

• Standardize and validate historical load inputs by converting inconsistent formats into kWh-ready datasets.

• Establish clear governance, communication, and reporting cadences across utility stakeholders and vendors.

• Integrate forecasting outputs into planning and capital investment workflows so insights translate into decisions.

• Maintain delivery momentum by resolving coordination friction and addressing interpersonal blockers directly and professionally.

  
  

SOLUTION

Tamazari acted as the “glue” that kept complex, multi-team implementations moving. We managed the day-to-day program needed to turn LoadSEER from a software purchase into a tool planners could actually use. This meant getting everyone aligned early, setting clear roles and timelines, and creating consistent ways to track work and decisions.

We guided the most error-prone part of the process—data readiness. We coordinated how each utility’s load and system data would be gathered, converted into the right format, and validated before forecasting models were configured. In short, we reduced confusion, prevented rework, and kept the program focused on outcomes. This allowed utilities to move faster from “we have data somewhere” to “we can see where growth is coming and plan for it.”

IMPLEMENTATION

Each utility followed a repeatable delivery pattern, adapted to local realities. We began with a discovery and data assessment to identify sources like GIS, SCADA, AMI, and billing. We clarified what would be needed to normalize and validate historical load inputs. From there, we coordinated data preparation, often the most underestimated phase, so models could be configured and calibrated against clean, consistent inputs.

In multi-vendor environments, we reduced friction by creating explicit agreements on collaboration tools and reporting cadence. A key example was Exelon, where coordination challenges between Integral Analytics and Deloitte were resolved through leadership alignment. We standardized shared workflows, including Smartsheet, that everyone could operate within. In tougher stakeholder situations, where a difficult working relationship risked disengagement, we intervened directly. We escalated appropriately and kept the work moving while maintaining focus on outcomes.

RESULTS

Utilities walked away with a much clearer picture of where load growth was building and what parts of the distribution system would get stressed first. Instead of relying on broad averages or waiting for problems to show up in the field, planning teams could use map-based forecasts. They could spot emerging hotspots and prioritize upgrades (feeders, substations, and capacity projects) earlier.

Just as important, the delivery approach proved repeatable. The implementation model and governance Tamazari used across earlier deployments carried forward into later ones, like SMUD. This made it easier to stand up LoadSEER efficiently, align stakeholders faster, and maintain momentum even in complex, multi-vendor environments.

LONG-TERM VALUE

Spatial load forecasting becomes more valuable as the grid becomes more dynamic. With electrification, EV charging, distributed energy resources (DERs), and compute-heavy loads reshaping demand patterns, utilities need planning tools that reflect geography—not averages. The long-term benefit isn’t only better forecasts; it’s better timing, better investment targeting, and better public outcomes.

When utilities can modernize proactively, communities experience fewer reliability surprises, more cost-effective infrastructure upgrades, and faster readiness for clean energy programs. This work ensures grid modernization isn’t just a policy goal; it becomes an operational reality that people can feel in the form of dependable service.

KEY LEARNINGS

• Servant-leadership PM unlocks technical excellence: The job is to remove obstacles and enable experts.

• Multi-vendor work needs explicit agreements: Tools, cadence, and formats must be decided early, or friction becomes a hidden schedule risk.

• Data conversion is never “just a step”: Plan for assessment, normalization, validation, and rework because forecasting quality depends on input integrity.

• Interpersonal issues are delivery issues: Avoiding hard conversations doesn’t protect momentum; it drains it.

• Repeatable delivery patterns scale across utilities: The strongest programs refine a method once, then adapt it thoughtfully—not reinvent it every time.

  
  

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