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Smart Meter Data Analytics & ML Toolset Development

Active

State & Local

Contract Overview

Solicitation details, issuing organization, response deadlines, documents, and interested companies for this government contract opportunity.

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This subcontract with the California Energy Commission focuses on the development of a smart meter-based toolset designed for prime contractors working on grid mapping projects. The scope of work involves designing algorithms for distribution line capacity mapping, integrating machine learning models with simulation tools, and creating decision support systems to facilitate utility load approvals. The project utilizes various ML frameworks and simulation software to deliver a functional software toolset. The project is based in San Francisco, California, and falls under NAICS code 541511 for custom computer programming services. It is structured as a subcontract aimed at enhancing the technical capabilities of grid mapping and load approval processes through advanced data analytics and machine learning.

General Info

CEC subcontract for smart meter toolset development using ML for grid mapping.

Agency

California → Energy Commission

NAICS

541511 - Custom Computer Programming Services

Place of Performance

San Francisco, CA, 94102

Set-Aside

NONE

Documents

This scope was carved out of EPC-25-055.

The full solicitation package (1 document), including the RFP, is on the prime solicitation, not on this scope.

View the prime solicitation

Line Impedance Network Estimator (LINE) for Grid Mapping via Smart Meters

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Timeline

PostedJul 17

subcontract

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Organization & Contact Information

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AgencyCalifornia → Energy Commission

ContactsNo contacts available

OfficeN/A

Organization / Agency

California → Energy Commission

Office AddressN/A

ContactsNo contact information available

Full Description

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Develops a smart meter-based toolset for prime contractors on California Energy Commission grid mapping projects. Designs and develops algorithms for distribution line capacity mapping, integrates machine learning models with simulation tools, and creates decision support systems for utility load approvals. Utilizes ML frameworks and simulation software. Delivers a functional software toolset for grid mapping and load approval.

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