Siemens Smart Infrastructure has partnered with CPFL Energia, one of Brazil’s largest energy distributors, to modernize the country’s electricity sector through an innovative smart metering project. The agreement deepens this collaboration and aims to increase CPFL Energia’s operational efficiency. That would provide major economic impacts and help safeguard a stable energy supply for domestic residential consumers.
The project demonstrates Siemens’ state-of-the-art technology. It includes the Gridscale X Meter Data Management system, which integrates easily with ERP and other applications. With the power of machine learning at its core, the initiative will help speed the identification of fraud, lower service costs and reduce service outages. CPFL Energia, serving 10.7 million customers across 687 municipalities, is poised to deliver improved energy services through this strategic endeavor.
“Digitalisation is vital to creating a sustainable and resilient energy future. Using Siemens’ technology, CPFL Energia will be able to improve its operational efficiency, as well as offer economic benefits and a reliable energy supply to household consumers.” – Sabine Erlinghagen
The partnership aligns with CPFL Energia’s goal to proactively address future regulatory demands and optimize the power grid in support of the energy transition. One of these is the recent acquisition of US-based Trayer Engineering by Siemens, that specializes in the medium voltage secondary distribution switchgear. This decision greatly strengthens Siemens’ position to provide the most advanced solutions for the project.
“In partnership with Siemens, our goal is to proactively meet future regulatory demands and optimise the power grid to support the energy transition.” – Evaldo Baldin
Siemens’ technology will significantly improve the pace and quality of maintenance, increasing facilities’ efficiency. This innovation holds the potential to deliver tangible and widespread economic benefits to consumers. With this expansion, CPFL Energia will better equip itself to identify and address the biggest inefficiencies in energy distribution. They’ll do this through smart data management and machine learning applications.