Challenge
Forecasting empowers industrial energy stakeholders to proactively plan for future energy demands, optimizing production, ensuring ample supply, and simultaneously reducing costs and energy consumption.
For industrial equipment users and manufacturers, forecasting serves as a valuable tool to identify anomalies in equipment performance or trigger preventive maintenance. By comparing predicted future behaviors with real-time data, potential issues can be addressed proactively.
Yet, accurate forecasting is not a one-size-fits-all solution. Common challenges include:
- Data Quality: Noisy or intermittent data can pose challenges.
- Data Quantity: Limited data availability for development.
- Model Selection: Choosing the most effective model is complex, given the diverse strengths, weaknesses, assumptions, and requirements of different models.
This undertaking demands substantial time and effort from a company's engineering team or may even require external assistance.
Project goal
The goal of this project is to create a powerful software library for precise forecasting across diverse scenarios. The lirbrary will use foundational AI models. These versatile algorithms (like ChatGPT - known for its natural language capabilities) will be applied to analyze data from various sensors. This approach not only streamlines the forecasting process but also minimizes the time and effort typically invested in trial-and-error cycles by engineers. The software library takes charge of addressing both data quality and quantity challenges, automatically selecting the optimal AI model for each situation. Notably, it operates seamlessly online, eliminating the need for specialized AI hardware.
Would you also like to participate in this project?
FAIF_IRVA is an industrial research project. We are looking for companies that want to be part of the user group and work with us to valorise the project.
Interested? Fill in the form below and we will contact you as soon as possible.
