Carbon Signal offers a data analytics platform for real estate portfolios to quantify and reduce operational carbon emissions. It uses machine learning and physics-based energy modeling to create accurate building performance models, enabling users to identify emission drivers and evaluate decarbonization strategies.
Funding
Funding not disclosed
Founders
Product
Problem
Real estate portfolios generate significant operational carbon emissions, and developing effective decarbonization strategies requires detailed analysis of building performance and energy consumption. Many organizations lack the specialized tools and expertise to accurately assess emissions, identify reduction opportunities, and model the impact of various interventions across their portfolios.
Solution
Carbon Signal provides a data analytics platform designed to help real estate professionals quantify and reduce operational carbon emissions within their building portfolios. The platform utilizes a hybrid approach, combining machine learning with physics-based energy modeling to create accurate digital representations of individual buildings. By ingesting basic building data such as size, location, and monthly energy usage, Carbon Signal calibrates these models to reflect actual performance. This enables users to evaluate existing conditions, pinpoint key drivers of carbon output, and identify specific decarbonization strategies with quantifiable energy and carbon reduction potential. The platform facilitates the creation of customized decarbonization scenarios, supporting comprehensive portfolio-wide planning and implementation.
Target Audience
The platform serves real estate professionals, including portfolio managers, sustainability officers, and building owners, who are responsible for managing and improving the environmental performance of their real estate assets.
Features
- Data ingestion capabilities for building size, location, and monthly energy consumption data via spreadsheet upload or third-party service integration.
- Machine learning algorithms for calibrating physics-based energy models to accurately represent building performance.
- Analytical tools to evaluate building performance, identify primary sources of carbon emissions, and assess reduction potential.
- Scenario modeling functionality to simulate the impact of various decarbonization strategies on energy use and carbon output.
- Portfolio-level analytics for comprehensive decarbonization planning and strategy development.
- Dashboard interface providing insights into emissions data and reduction opportunities.