Master uncertainty
in electricity markets
Versiro develops software that accelerates the energy transition. Renewable electricity is great for the environment but difficult to plan because its production depends on uncertain weather conditions. Versiro develops an AI copilot that helps renewable producers make smart decisions in real-time while considering these uncertainties. This assists the integration of renewable energy and increases its profitability, two key factors to enable the green energy transition.
Increase profits. In real-time.
We provide software that quantifies and analyzes uncertainty in electricity markets, with a specific focus on intraday markets.
This answers questions like what price to set for your energy and the probability that an order is accepted. Everything in real time and updated when new information enters the electricity market.
Modern technology for a modern power system.
Our probabilistic methods are valuable for actors impacted by fluctuations, from renewable energy producers to managers of storage assets or utilities.
We use machine learning to predict future scenarios in intraday and balancing markets and then use statistical analysis to evaluate these scenarios with respect to individual assets, risk profiles and trading positions.
Features
Quantify and analyze uncertainty
Find the right price for your asset, risk profile, and trading position.
Plan confidently in real time
Receive trading strategies that updates whenever new information enters the market. Know whether to trade now or wait for future opportunities.
Accessible state-of-the-art tech
Access state-of-the-art algorithms that Versiro continuously improve easily through a web platform or API.
Increase profits
Monetize on flexible asset by responding to price volatility or reduce cost of imbalances by trading intraday instead of being penalized in balancing markets.
High-tech, yet highly accessible
Versiro was founded by researchers from the departments of Electric Power Engineering and Industrial Economics and Technology Management at the Norwegian University of Science and Technology and follows the observation that, in electric power systems, the traditional pipelines from research to industry are too outdated to keep up with the fast-paced world of machine learning.
Team
MSc in Electric Power Systems, NTNU Trondheim
Former researcher at NTNU
MSc in Supply Chain Management, WU Vienna
BSc in Business Informatics, WU Vienna
Former researcher at SINTEF Energy, NTNU, and WU Vienna
BEng in Industrial Technologies, Universidad Politécnica de Cartagena
Work experience from DNX Ventures and Actelligent Group