The Automated Carbon Model (ACM) is Climative’s powerful machine learning algorithm used to quickly and cost-effectively evaluate the energy efficiency and carbon footprint of homes. The traditional approach to home energy assessments only covers 1-3% of the building stock annually, with a post-audit retrofit rate of about 0.7%. At this rate, it will be beyond 2150 before we evaluate and retrofit all homes to net zero. The Automated Carbon Model was created to greatly accelerate energy assessments and drive impactful home retrofits. The ACM‘s high-quality home carbon data output has many uses to help banks, governments, utilities, and homeowners achieve their net zero goals.
Click here to request a copy of the full ACM whitepaper.
The Automated Carbon Model is Climative’s algorithm for quickly and cost-effectively evaluating homes’ energy efficiency and carbon footprint on a large scale.
Trained on 1M+ on-site energy assessments, results are about 80% accurate. This estimate requires fewer than ten inputs, compared to 200+ for an on-site assessment.
These energy and emission insights are useful for carbon reporting (PCAF), net-zero policy, efficiency program design, homeowner engagement, and building labeling.
The Automated Carbon Model (ACM) is Climative’s advanced algorithm for evaluating the energy efficiency and carbon footprint of residential properties on a large scale. The ACM powers Insights and Navigator, components of the Climative Platform.
The Automated Carbon Model (ACM) is used to quickly generate valuable emissions insights for buildings, such as estimated annual energy consumption, estimated greenhouse gas emissions, a breakdown of energy usage, recommendations for energy efficiency upgrades including potential cost savings, projected GHG emission and energy efficiency ratings for each assessed home.
Aggregate insights generated by the ACM are well-suited for policy design, efficiency program design, carbon reporting, building labeling, and more.
For individual homeowners, the ACM produces a “first pass” assessment based on available data. The assessment can be made more reflective of the building, thus more accurate and actionable, when a homeowner adds more info in the questionnaire, linked billing data, and attached results from an on-site assessment. Climative suggests that homeowners speak to an energy efficiency professional before investing in any efficiency retrofits.
Click here to request a copy of the full ACM whitepaper book a call to learn about use cases.
The ACM is trained using over a million on-site energy assessments conducted by registered energy advisors. The analysis can consider utility bills and census data, ensuring that the AI models are accurate compared to on-site energy assessments. The model also considers geographical and demographic factors when providing energy retrofit recommendations.
The ACM generates energy insights with very few inputs: fewer than 10, compared to over 200 collected during a typical on-site assessment. Public data (such as property assessment, energy costs, and weather data) can be used to generate a comprehensive low-carbon plan. Layering in additional data from surveys, remote assessments, and on-site assessments can further improve the accuracy and helpfulness of each low-carbon plan.
The Automated Carbon Model (ACM) assesses residential buildings’ energy efficiency and carbon emissions across regions without requiring an on-site visit. It considers building characteristics, geographical location, weather impact, and detailed information about the building, such as furnace age and building envelope insulation, obtained through homeowner surveys, remote assessments, and even on-site assessments.
Using building science-based models and machine learning techniques, the ACM is trained and tested on on-site assessments conducted by registered energy advisors.
The output of the ACM (called a home energy report, home energy assessment, or low-carbon plan) helps homeowners better understand their houses in terms of energy efficiency and carbon emissions. Furthermore, it provides homeowners with customized and actionable energy retrofit recommendations to speed up their transition to net zero.
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Automated Carbon Model ™ and ACM ™ are trademarks of Climative.
Tian is a dedicated and creative statistical modeler who leads the development of Climative’s machine learning algorithms. She believes that ethical and thoughtful use of AI will help us achieve climate goals while creating equal opportunity for all.
Climative provides a collaborative AI-assisted data platform for organizations to enable personalized advice and offers to building owners, taking the guesswork out of building upgrades and transforming the low carbon economy.
