The green energy future will be powered by real-time, machine learning technology

Introducing our new product, Origami Forecast.

This year we are seeing a glimpse of the physical and market volatility we can expect in the future.

At Origami our mission is to help energy companies make better, faster decisions within an energy system increasingly dominated by renewables. This is particularly critical in the context of Covid-19, with long-term forecasts and plans needing significant re-work in the hours ahead of delivery. And, with renewables supplying a much higher proportion of total energy, we are seeing a glimpse of the physical and market volatility we can expect in the years ahead.

High quality forecasting is critical in order to make informed trading decisions. 

At the risk of stating the obvious, forecasts are an absolutely central part of decision-making, particularly for low-carbon assets where decisions depend increasingly on opportunity cost rather than fuel cost. The need to compare commercial options across an increasing number of short-term markets only adds to the importance of being able to somehow “predict the future”.

The good news is that technology – and in particular machine learning – has advanced massively over the past few years. But there are big challenges in maximising the value extracted within energy markets. The sheer scale of real-time data can be overwhelming both in quantity and format. Traders often lack the tools to understand and then release forecasts into their position and it can be hard to establish a repeatable and scalable capability given the scarcity of skilled data scientists and engineers. Being able to focus on what really matters at the right time is critical.

Introducing Origami Forecast

In that context, I am delighted to launch our new product, Origami Forecast, powerful enterprise forecasting for energy companies that delivers automated, live predictions.

This marks an exciting step change for Origami. The machine learning decision engine that forms the heart of the Origami forecast product is being added to our platform’s existing real-time dispatch and monitoring capabilities.

You can read the product features here, but I am most excited about:

• Our versatile data ingestion framework, which simultaneously pulls together hundreds of real-time physical, market and weather data-streams for use within our forecast models
• A highly automated prediction platform, utilising leading machine learning algorithms to produce and improve live forecasts at scale
• A rich ability to visually interrogate, compare, adjust and release forecasts into your trading position
• A set of tools that will enable users to pipe in their own data, refine existing models and create their own forecasts, without needing to develop the full tech stack themselves

Over the past year, we have been working closely with our key partners to refine this capability – testing its performance on wind, solar and system price forecasts. Early results have convinced us that our ‘Forecast’ product will be able to support any energy company looking to develop or scale up its forecasting capabilities.

Of course, forecasting is just part of a bigger digital jigsaw puzzle that energy companies are trying to solve. Origami has a library of interoperable and configurable software modules to meet the challenges of increasing complexity and volatility head on. Energy companies will be able to optimise decisions in real-time - improving their trading performance; unlocking exciting new customer propositions; and investing in low carbon assets to accelerate the energy transition.

We are entering the green energy future, and I believe successful energy companies will need to combine their market expertise, customer insight and smart capital with the intelligent, scalable automation made possible by innovative machine learning software such as Origami Forecast.

Over the coming months, we will be launching more products from our growing suite of interoperable software modules, all aimed to optimise energy trading.

Watch this space!