Our secret master plan

Our secret master plan

As many know, our initial product was computer vision algorithms to estimate productivity and emissions from large fossil fuel facilities in regions where data is unavailable, untimely or unreliable. This innovation laid the foundation for Climate TRACE, a coalition of non-profits and former U.S. Vice President Al Gore, offering a global, independent inventory of greenhouse gas emissions.

As our expertise has grown, so has our ambition. Not content with monitoring the problem, we wanted to enable solutions to help countries transform their economies with clean, affordable, and reliable energy.

This is why we are developing Scenario Builder: a global energy system modelling platform that provides net zero scenarios. Even so, some may question whether this does any good? Do we need another data initiative? The answers to these questions are ‘not much’ and ‘no’. However, that misses the point – unless you understand our secret master plan.

Ambitious and urgent policy changes are required before electricity is clean, affordable and reliable for everyone. Those changes are most likely to be implemented in a world where all countries and organisations can do robust and responsive transition planning. Critical to making this happen is the widespread use of energy system modelling. Here’s why: most decarbonisation will occur through electricity, and most investment decisions in the electricity sector require energy system modelling.

The problem

Energy system modelling today has significant barriers: it is expensive, inaccessible, and unscalable. Consultants often take months or even years, with each study incurring substantial costs and additional time. Commercial software is costly and often demands specialised training or expertise to use. Academic tools, while free, require coding skills and technical knowledge, limiting their usability. Gaps in software engineering standards and limited user-friendliness hinder the integration of these tools into broader workflows, making them challenging to scale and adopt for real-world applications.

This situation causes inadequate and slow transition planning. As a result, investment is limited, especially in developing countries. To solve this problem, our platform builds capacity – not dependency. We enhance transparency and accessibility with open-access data and an end-to-end modelling service for technical and non-technical analysts alike. The question is how are we going to do this?

Our solution

Build an open and accessible platform for energy systems modelling

Analysts span a technical spectrum, ranging from no-code and low-code users more confident in spreadsheets, to programmatic users with advanced coding skills. Despite many open initiatives in energy system modelling, broader adoption remains elusive. Beyond requiring modelling expertise and facing integration hurdles, these efforts overlook a simple truth: many analysts don’t code and never will.

Our plan is to build an energy system modelling platform that transforms no-code analysts into experienced modellers. At first, the platform will offer basic functionality for no-code users, using a simple and intuitive user interface. The driving metric of our engineers is to minimise the time required to create, compare, and share high-quality scenarios and simulations.

Over time, we plan to add functionality that lets no-code and low-code analysts do everything an experienced modeller can, making energy system modelling tools available to more people. We have already built the first version of the final step in the workflow, Results Viewer, where users can view and share the results of the scenarios they have created.

Explore our latest models in Results Viewer.

Create ‘model-ready’ data and ‘scenario-ready’ models

Most of the time required to create scenarios and simulations is spent on data wrangling and model calibration. Data collection, processing, and maintenance account for over two-thirds of the effort in new energy system modelling projects and nearly half in ongoing projects. The remaining time is typically spent calibrating the model to ensure it aligns with real-world conditions and accurately represents the grid’s behaviour under different scenarios.

We plan to create 'model-ready' datasets – processed, standardised, and validated – to integrate seamlessly into the platform, letting users focus on user insights, not grunt work. To ensure the platform is accessible and scalable, we will enable users to upload their own data and introduce a private partnership program to help us to calibrate our stock models in markets where we lack expertise.

We have made significant progress in addressing data quality gaps to ensure our datasets are robust and reliable. For instance, Solar Asset Mapper (TZ-SAM) leverages satellites and AI to monitor solar PV assets globally. This provides accurate, up-to-date data critical for energy system modelling. Similarly, the Coal Asset Tool (TZ-CAT) delivers plant-level power purchase agreement (PPA) estimates, allowing the development of more realistic scenarios and simulations where coal phase-outs are driven by PPA renegotiations.

Accessible content and community

We want to help more teams working on energy transition planning use system models to make better decisions, faster. To do this, analysts of all shapes and sizes need to become confident energy systems modellers.

Our platform is purpose-built to tackle the unique challenges of energy system modelling – a niche, highly specialised field. This reality needs to shape our approach to both content and community strategy. The same barriers to openness and accessibility that exist in energy system modelling tools are mirrored in the content surrounding the field: most publicly available resources tend to be too academic or technical, leaving busy, real-world analysts without educational and actionable insights.

Just as our platform reduces time to create high-quality scenarios and simulations, our country analysts create content that tells users, in plain english, how to use energy system models effectively. We hope to create a feedback loop, where content, community and platform development support each other. This approach will be extended to community engagement. Openness and accessibility will remain central to how we engage with our partners and users, fostering collaboration and amplifying their efforts.

Owning the ecosystem

In summary, our master plan is to:

• Build an energy system modelling platform that transforms no-code analysts into experienced modellers.
• Innovate to minimise the time it takes to create high-quality scenarios and simulations, allowing users to focus on insights.
• Provide targeted content designed to break down knowledge barriers and enable effective use of energy system models.

In an increasingly open world, owning the ecosystem is the only way to win. By owning the platform where innovation happens, we can cement ourselves as thought-leaders and direction-setters, allowing us to shape the narrative on ideas larger than ourselves. In doing so, we can play our part in helping countries transform their economies with clean, affordable and reliable electricity. Like any start-up, there will be bumps along the way. What matters is how we learn from them, adapt, and keep moving forward. You can stay updated here.

Don't tell anybody.