The science behind Carbon Signal

How Carbon Signal combines machine learning models with physics-based energy modeling techniques to help portfolio owners decarbonize.

The science behind Carbon Signal


Shreshth Nagpal

March 25, 2024

Carbon Signal is an analytics framework that systematically explores decarbonization pathways for real estate portfolios by developing a comprehensive set of multi-criteria optimized solutions for carbon reducing strategies using auto-calibrated building energy models.

From a technical perspective, most analytics platforms utilize one of two approaches to model building performance and estimate the impact of various decarbonization strategies. The first approach is using a data model, which might involve database lookups, typology-based assessments, or regression analysis (or some combination of all three) to try and generate insights from observed patterns in the data. These platforms offer roughly the same level of analytics as one could generate from a spreadsheet tool, and they’re great for getting quick answers to simple questions about energy performance, especially at the scale of a portfolio.

The other approach is using a deterministic model, which involves detailed inputs and complex physics-based calculations. Specialized consultants will collect relevant information about existing building characteristics, feed that as an input into building energy models, and then utilize these “calibrated” energy models to simulate achievable energy and carbon reductions with different potential interventions and upgrades. This approach is great for answering more complex, building-scale questions about energy performance, but the process is notoriously time and cost intensive and the results are heavily influenced by modeler judgement and (usually) informed by nothing more than trial and error.

Most approaches to model calibration involve time-intensive guess-and-check techniques that don't scale for large portfolios. Image © Winsonsinart

Under the hood, Carbon Signal uses deterministic energy models to analyze energy use, but we use machine learning to speed up and improve the calibration process. This approach combines the best of both worlds: the ability of deterministic energy models to estimate energy flows, and the ability of machine learning to rapidly scale up the calculations to account for hundreds of thousands of variations across a wide array of possible unknowns. The result is a framework that is not only technically superior but also highly extensible. With any specific strategy in a specific building, for example, Carbon Signal can determine the likelihood of achieving a certain range of energy and carbon reductions, even when there is significant uncertainty in he underlying conditions of the building. As the model gets refined over time, the platform automatically recalculates confidence intervals, shrinking uncertainty bounds by closing in on more constrained design options.

How accurate is Carbon Signal?

The short answer is that we consider our analytics to be on par with an ASHRAE level2 audit. Traditionally, the audit process is handled by specialized consultants who complete detailed engineering calculations based on in-person surveys of existing conditions and data-collection from several sub-metered energy use components. This approach to determine the likely impact of potential interventions has been used and validated for decades in the building engineering industry. But it also sets you back, best case, several weeks and several thousands of dollars, per building, for every building. 

The longer answer is that Carbon Signal is built on the hypothesis that any decision-making framework is better served by rapid-response probabilistic models that yield directionally accurate results instead of highly deterministic models that risk being rendered wholly inaccurate with just a few incorrect assumptions. Directional accuracy means the results point towards the correct set of actions with a quantifiable level of uncertainty. Precision, in this case, is derived from probabilities rather than a single predictive element.

This puts less weight on any single assumption, meaning the results are still useful if that assumption turns out to be wrong, and given the inherent uncertainties associated with building operations, this is arguably a better approach to modeling energy use. Carbon Signal enables stakeholders to quickly identify all high-priority projects across a portfolio, ultimately helping them to identify areas for further targeted investigations, while fully understanding the real-world risks and uncertainties associated with their implementation plans.

Image © Oliver Sved

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