Integration of AI tools for energy applications
/
Integration of AI tools for energy applications
Companies realize that further digitization is necessary to continue innovating, growing, and making their processes more efficient. There is a growing demand from companies on how they can be supported in the energy transition without incurring significant costs.
Artificial Intelligence (AI) can be a solution in this regard, but due to ignorance, it is perceived as too complex. Additionally, SMEs do not have the knowledge on board or do not have the time. As a result, the added value of AI tools cannot be accurately assessed, and nothing is done with it.
We want to support SMEs and enable them to make informed choices in the digital transition. More specifically, we aim to bridge the knowledge gap between the research and business worlds. After discussions with companies, there is significant interest in this theme. Several enterprises are participating in the EnergAI project because they want to gain deeper insights into AI integration and the support needed for this.
Main section
Quick facts
/
TETRA project
/
Location: Kortrijk
/
Start: October 2022
/
Duration: 2 years
What is the goal of this project?
Various case studies demonstrating that predictive flexibility and predictive maintenance are implementable for energy savings in Flemish SMEs, through:
- Network: We collaborate with 6 sector federations, the spearhead cluster Flux50, and other universities/colleges that help disseminate the acquired knowledge through study days, workshops, articles, …
- New landing page of the Smart Tech cluster (Howest ENM + MCT): should enable the intended broader target audience to access all acquired AI knowledge from ongoing and past projects in a centralized location. This allows companies to facilitate faster AI adoption based on the provided advice and save a significant amount of time in achieving results. We expect a time saving of 4 to 6 person-months for total projects (from sensor to predictive model) per company that wishes to implement this knowledge.
- Predictive models that lead to energy optimizations and savings. Predictive flexibility and maintenance are expected to achieve a reduction in fuel consumption (10%), energy storage capacity (25%), and energy system (5%).
- Dissemination and integration into the educational offerings, which creates a broad support base. There is a wider dissemination of knowledge possible through various programs that have all implemented a component of AI and energy in their curriculum. The ambition is to deliver at least 50 readily deployable profiles from education to the market to implement this knowledge at the end of the project (10 after year 1) (KPI3).
Bottom section
What is the role of Howest?
- Proactively raising awareness among the target group through 4 specific use cases (KPI2 – documented validations) within the theme of AI forecasting, more specifically around themes decided by the assembled advisory group. By building knowledge on how a powerful AI model can be brought into production with optimized time investment (1 case after 1 year, 4 at project completion). Based on previous projects, it can be stated that after 1 year at least 20 interested companies will be reached and > 50 will apply the knowledge by the end of the project (KPI1).
- 2 real-life demonstrators (KPI2) in hardware and software that convert the knowledge of the use cases into generically translatable information for dissemination within the broader target audience (e.g., Cleantech hub Snowball).
- Development of the EnergAI guide, which will gradually translate the knowledge from the project from sensor to AI model to the predictive behavior of the system (e.g., # AI forecasting techniques, efficient data management, developed demo cases, how to efficiently deploy an existing AI model in production, etc.).
- New landing page/website for disseminating project results from ongoing and completed AI projects. So that companies can find targeted information on AI themes and through this page can find concrete assistance through guides (e.g., EnergAI guide), information from study days, workshops, etc. The aim is to reach at least 300 unique companies that gather concrete knowledge through the website after completion (50 after year 1) (KPI4).
- Communication and Dissemination:
- 4 targeted workshops and an internal study day (on-site + online events).
- At least 5 articles through sector federations and a speaker at 4 external networking events where further knowledge dissemination to the broader audience will be achieved.
Contributors
Researchers
/
Paula Acuna Roncancio , Onderzoeker
/
Arne Bauwens, Onderzoeker
/
Henk Bostyn, Onderzoeker
Want to know more about our team?
Visit the team page