Independent energy consultancy Utility Trade has begun a three-year collaboration with De Montfort University to develop an artificial intelligence system to improve pricing accuracy and energy demand forecasting for business customers.
The project is being implemented through the Knowledge Transfer Partnership, a national program that brings together business and academic expertise to support innovation and productivity. The partnership is valued at £270,000, with 67% funded by Innovate UK and the remaining investment provided by Utility Trade.
The effort will focus on building an AI-driven pricing and forecasting platform that automates internal processes, enhances data modeling, and improves the speed and reliability of commercial decision-making. The system is expected to support faster client response times, more consistent reporting and billing, and more accurate forecasting, allowing organizations to ensure utility contracts that reflect actual demand patterns.
Founded in 2010 and based in Market Harborough, Utility Trade advises UK businesses on energy sourcing, cost management and efficiency. The partnership aims to embed advanced capabilities in software development, data management and cybersecurity directly into the consultancy's operations.
Academic contributions will come from De Montfort University's technology and business experts and will be supported by the recruitment of two graduate students with expertise in data modeling and software development.
Darsheet Chauhan, Head of Knowledge Exchange at De Montfort University, said: “We are really excited to be working on this dual associate KTP project with Utility Trade.DMU’s combined expertise in software development and data management will allow KTP to work with Utility Trade in “This project will provide tremendous growth potential for the utility trade, supporting our path to becoming a sector leader and boosting local business.” Promoting job opportunities and advancing UK businesses' sustainability goals. ”
The initiative reflects a broader move toward data-driven pricing tools across the commercial energy sector as market volatility increases and procurement decisions involve greater financial and operational risk for companies.
