WattTime’s purpose is to help others solve climate change as fast as possible. We produce data that enable all the good work that others are doing to go farther and faster than it otherwise would. The foundation of making more impactful decisions is to measure the truly causal impacts of those decisions. Our data are not meant to simply be included in a report once a year, they are used by organizations that want to maximize the authentic impact per dollar they invest. If every project or initiative is made twice as effective by using better data, that could mean we achieve the goal twice as fast.

For an emissions model to achieve its desired purpose, accuracy alone is not enough. Different types of emissions rates apply to different questions being asked, so first and foremost, the appropriate rate should be applied for a particular use case. The data being used should apply to the relevant location and to the time period during which an intervention will be deployed.

WattTime publishes new marginal emissions data every 5 minutes, including a forecast of the next 72 hours, because stale data results in lower effectiveness. We advise our partners about the best available data for each individual use case, whether or not the data are our own. We invest heavily in not only improving our modeling techniques but also validating that the use of our models produces real emissions reductions. We collaborate with other electricity grid and emissions scientists, grid operators, and technology organizations to advance the science of marginal emissions. In places around the world that are lacking in publicly available emissions data, we figure out ways to measure it ourselves.

To learn more about our approach to methodology & validation and our goals for future research, see our paper from 2022.

WattTime has formed a working group to advance the science of marginal emissions validation, called VERACI-T. Learn more here.

WattTime research produces data that answer the question, “What is the change in emissions caused by a change in electricity use (or generation) at a particular time and place?” Academic research refers to this type of data as marginal emissions rates.

WattTime staff over the past decade have extended the basic techniques in peer-reviewed published literature on estimating marginal electricity emissions, including this peer-reviewed literature co-authored by our founder Gavin McCormick. This technique is a specific instance of a widely accepted class of marginal emissions analysis models used in numerous peer-reviewed articles. A selection of these papers are included below for reference.

As our research has produced many iterative advancements, we have kept a focus on the fundamental reliability of causal, empirical modeling, while also making them more useful for active emissions reductions and controlling devices in real-time.

Please contact us for a more detailed step-by-step description of our latest modeling methodology, which we can make available upon request.

The fundamental approach of marginal emissions models in the literature is very similar: they start with raw electricity generation and emissions data available from the US EPA at an hourly frequency by every major fossil fuel-fired power plant in the United States (via continuous emissions monitoring system, CEMS). A similar approach applies in other countries where data are available. Each then applies regression-based modeling to ask, every time a rise or fall in electricity demand occurs in a given place and time, which power plants actually increase or decrease their output in response? This allows a modeler to compare how marginal emissions rates vary by time and place. For example, by running such regressions separately for day and night periods, such models can compare the marginal emissions caused by using electricity during the day or during the night.

WattTime believes strongly in such empirical modeling techniques because they are driven almost purely by the data and require almost no assumptions. This is part of a growing trend in economics and social sciences towards increasingly questioning the validity of assumption-driven models, and replacing them instead with almost purely data-driven causal models, a trend known as the “credibility revolution”. The most notable proponent of this approach, Esther Duflo of MIT, won the 2019 Nobel prize in economics for this innovation.

WattTime staff have run numerous tests of how much Esther Duflo’s fundamental insight of questioning assumption-driven models applies to the energy sector and shown that most basic assumptions often fail to hold up in the real world. These and other insights have deepened the WattTime team’s conviction that empirical models are essential for accurate marginal emissions analysis.

WattTime has continuously improved our modeling techniques rooted in literature while keeping the fundamental reliability of causal, empirical modeling as a foundation. The established empirical approach above works well for historical datasets. We’ve also made them more useful for applications that use the data to reduce emissions, such as controlling devices in real time.

For example, it is one thing to say emissions are cleaner during the day than at night, but quite another to measure slight variations every five minutes. So for years, WattTime’s models have leveraged real-time power grid data from individual grid operators as well as the Energy Information Administration to detect very fine-grained changes in power grid behavior that are highly predictive of patterns when marginal emissions are higher or lower. Furthermore, because CEMS only measures fossil fuel plants, standard models cannot detect the increasingly common moments when non-fossil (e.g. solar) plants are marginal. So, WattTime integrated additional datasets that measure moments of renewable energy curtailment, allowing us to build predictive models for when curtailment is occurring.

No model is perfect, and WattTime is continuously researching new modeling techniques and methods for validating them. Our goal is to constantly improve the efficacy and feature set of such models to enable ever-greater emissions reductions.

Our empirical modeling is highly dependent on the availability of relevant data, which has so far been exclusively from publicly available sources. Where possible, our models are derived from historical time series data including:

In many electricity grids around the world, not all of the data above are made publicly available by grid operators or governments. Nevertheless, there is still great opportunity for emissions reduction interventions in places without public data and for technology companies that are capable and eager to deploy interventions in those locations.

Initially, our priority was to produce models of the highest quality while expanding our coverage into regions that have adequate publicly-available data to produce these high-quality models. After completing our coverage of places with top-tier public data, we expanded our coverage to include a dozen countries in the next tier — places with partial data available. Most recently, we expanded our coverage globally to cover a total of 210 countries/territories and 99% of global electricity consumption.

For regions that lack complete public data, WattTime has developed alternative methods of estimation to make the most of the data that are available. Model accuracy suffers as we rely on less endogenously reported and directly measured information, however, we aim to produce a marginal emissions signal that is still effective at producing meaningful emissions reductions from load flexibility and/or renewables siting. The result is a hierarchy of models that has allowed us to expand the reach of our impact to every corner of the globe.

The efficacy of avoiding carbon emissions by load shifting can be greatly improved with real-time estimates of the emissions impact of those shifts, instead of using historical averages. Even better performance is achieved when using a forecast to rank all of the intervals within the flexibility horizon and scheduling consumption during the lowest impact intervals. WattTime has been publishing forecasts of marginal emissions rates since 2019.

How does your forecast model work?
We currently produce forecasts of CO2 MOER and Health Damage with a horizon of 72 hours. WattTime uses an ensemble of ML models trained on the historical MOER signal. There are multiple features in that model, including ARIMA-style features and exogenous features. And we directly forecast each time horizon. Our MOER forecast is similar to a weather forecast, which has better accuracy and precision in the upcoming hour (e.g. rain at 10 o'clock) than it does 3 days out (50% chance of rain on Thursday); the MOER forecast can inform very particular timing in the first few hours, and broader scheduling patterns more than 24 hours in the future.

How accurate is your forecast?
We forecast performance primarily using an efficacy metric, in addition to expected rank correlation and mean absolute error. We measure efficacy by comparing the emissions reduction performance of load-shifting using (a) our forecast and (b) perfect information (knowing the emissions rates in advance). This performance efficacy metric helps us gauge how well we are capturing the carbon reduction opportunity when load is scheduled based on the forecast. This is substantially a function of predicting the rank order of MOER in upcoming time intervals (predicting the timing of the peaks and valleys is the most important aspect of the forecast for this use case).

Forecast efficacy varies by grid region and is heavily dependent on the generating resource mix and unpredictability of marginal emissions rate. Wind-dominated regions (i.e. the great plains of the US) tend to be harder to predict than solar-dominated regions (i.e. California). Most grid regions have a forecast efficacy of 50-90% carbon reduction compared to a perfect forecast. The MAE for most regions falls between 1% to 9%.

Marginal emissions are not directly measurable, so validation is very important to ensure that our research progress translates to more effective real-world carbon reductions. Without ground truth, it can be challenging to determine which models are closer to the truth and quantify their accuracy.

WattTime welcomes peer review of our methods and has continued to publish literature on our methods. In 2017, RMI conducted further validation of WattTime’s methods and their application to automated emissions reduction. Many of our partners that build technology features that rely on our data have performed their own evaluations of our data; some of those partners have formed coalitions to advocate for the use of marginal emissions in GHG accounting standards.

WattTime doesn’t only validate our own models, we’ve started collaborating with other organizations to evaluate their own emissions datasets and to share best practices. The working group that WattTime convened is called VERACI-T (Validating Emissions Rates for Accurate Consequential Impact Taskforce). By learning from other organizations modeling marginal emissions and other electric grid phenomena, we hope to more rapidly advance the techniques for validating marginal emissions models and improve the accuracy of the models, in order to drive greater impact.

The method for determining marginal emissions rates builds on the techniques in these academic papers, including one written by our founder, Gavin McCormick.

Many researchers and organizations have used marginal emissions data (produced by WattTime or others) in their research, decision-making strategy, and product design. Below are those we’re aware of. Please reach out to us if you’d like to use WattTime data in your research.