inBalance Is Funded by Y Combinator and Joins W21 Batch

Today we’re excited to have TechCrunch share our story as we announce joining the YC W21 batch.

Recent extreme events in Texas showcase why electricity price forecasting is so crucial—and why we’ve built the Delphi forecasting system. We use machine learning to accurately forecast electricity prices from terabytes of public and proprietary data. The solution required for daily power system stability is both hardware—like storage and electric vehicle charging—and the software required to optimally use it. inBalance exists to be that software solution.

To Optimize Renewables, We Need Better Forecasting

Electricity prices change every 15 minutes, and sometimes substantially. Even without extreme climate events like those in Texas in February, prices can double in a matter of hours. That makes forecasting a crucial tool for all utilities. As prices spike, asset managers of finite responsive resources such as hydro and storage need to decide if they will offer more value to the market now or later. Coming online too early or too late will decrease the revenue for their clean generation and increase peak prices for consumers.

The situation is even worse if storage comes online simultaneously with intermittent renewables, as the storage will create downward price pressure for both itself and intermittent renewable assets. Often, this overlapping of renewables generation and storage discharge leads to increased fossil fuel generation later the same day, once cleaner sources are depleted.

Helping Market Participants Make the Most of their Renewable Assets

Decarbonization requires that clean flexible generation asset managers and storage providers have the tools they need to anticipate when they are most needed. Rather than trying to replace asset managers with autonomous dispatch tools, we support asset managers by providing price forecasts and an analysis of the factors driving the market.

Forecasting one in a hundred year events is a challenge. We have worked hard to make sure that our model is not a black box so that asset managers understand how Delphi is making its forecasts during abnormal market conditions. Ultimately this will improve grid reliability during extreme weather events, which are becoming more frequent due to climate change.

Where We’re Headed

The quality of our models has already earned the trust of our clients, among them a top ten US utility. At the same time, we’re thrilled to contribute to a measurable decrease in fossil fuel emissions across New England, one of the many markets in which we currently offer forecasting services.

Our goal is to ensure that every asset manager has the best possible forecasts at an affordable cost. By allowing market players to unlock the maximum value of their clean energy, inBalance becomes the machine learning driver of decarbonization—and, in the process, accelerates the transition to a world of stable, clean power.