An International Energy Provider
Predictive energy forecasting for smarter, lower-risk trading.
Energy suppliers operate under a structural commercial risk: every megawatt bought above or below actual demand carries a financial penalty. As markets fragment across borders and forecasting becomes a competitive lever, the operators who win are the ones who can predict consumption accurately and bid with confidence.
A strategic energy technology partnership
One intelligent system to manage balancing obligations and trading decisions.
The client is an international energy products and services provider operating as a Balancing Responsible Party across multiple markets. Before partnering with Qubiz, the client ran separate legacy solutions across three different countries, leaving energy trading exposed to mis-forecasted volumes and avoidable costs. Together, we designed and built a unified BRP application that consolidates these systems and adds a predictive layer on top, balancing consumption and generation as close to zero as possible in each trading interval, and turning a previously reactive process into a data-driven one.
A single predictive platform replacing a fragmented legacy landscape.
We replaced multiple legacy solutions with one application covering every function tied to the Balancing Responsible Party role — with a machine-learning forecasting core at its centre.
- 01
One operational view
The platform brings consumer profiles, energy production, and consumption data into a single operational view, removing the inefficiencies of running parallel systems across markets.
- 02
Short-term demand forecasting
A machine learning module generates short-term electric demand forecasts from consumption patterns, telling the business exactly how much energy to buy in advance.
- 03
Accurate market bidding
Predicted consumption is translated into accurate market bids, helping the client commit to the right volumes at the right time and reduce exposure to penalties.
- 04
Balancing to zero
The application actively balances consumption against generation so the gap stays as close to zero as possible in each interval — continuous, predictive, automated.
THE SOLUTION
Unified BRP platform
We replaced multiple legacy solutions with a single application covering every function tied to the client's Balancing Responsible Party role. The platform brings consumer profiles, energy production, and consumption data into one operational view, removing the inefficiencies of running parallel systems across markets.
Short-term electric demand forecasting
At the core of the solution sits a machine learning module that generates short-term electric demand forecasts based on client consumption patterns. It tells the business exactly how much energy to buy in advance, addressing the core commercial risk that energy suppliers face every trading day.
Accurate market bidding
The platform translates predicted consumption into accurate market bids, helping the client commit to the right volumes at the right time. By aligning purchasing with forecast demand, the business reduces its exposure to the financial penalties that come with buying too much or too little energy.
Continuous balancing in every trading interval
The application actively balances consumption against generation so the gap stays as close to zero as possible in each interval. This continuous, predictive approach makes energy trading measurably more time and cost efficient, replacing manual reconciliation with intelligent automation.
What it enables
From reactive reconciliation to data-driven trading.
Unified
A single BRP application replacing separate legacy solutions across three countries.
Predictive
Machine-learning short-term demand forecasts that tell the business how much energy to buy in advance.
Accurate
Predicted consumption translated into market bids that reduce exposure to financial penalties.
≈0
Consumption and generation balanced as close to zero as possible in every trading interval.
The operators who win are the ones who can predict consumption accurately and bid with confidence. The predictive layer turned a reactive process into a data-driven one.
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