Utilizing Artificial Intelligence and Machine Learning to Improve the Energy Efficiency of Industrial Processes and Buildings

The aim of the preparatory project is to prepare a significant project for the 2024 call.
1) Identify and delve into the necessary data sources and actions required for the research topics.
2) Gather the final consortium for the project and engage the corporate partners in the project's objectives and implementations. (OAMK),  (JAMK), and (TAU) have been involved in the project preparations, along with discussions already held with seven companies on the matter.
3) Establish a network and ecosystem.
4) Prepare the application in collaboration with the partners participating in the actual project (the project consortium, as per goal 2) and submit the application for the actual call for proposals.

The methods of artificial intelligence and machine learning have increased in popularity in several industrial sectors in recent years. This is due, among other things, to the growth in computing power of computers, the development and widespread adoption of machine learning methods. The amount of data stored from industrial processes has significantly increased due to the increased and cheaper data storage capacity. In addition to local data, data from various open sources can be combined with the measurement data of target processes, such as weather forecasts and SPOT electricity price information. From a large amount of data, machine learning methods can be used to calculate indicators related to the production system or to identify possible deviations from normal operating conditions.
An AI-based system adds value, especially when processes or properties can be continuously adjusted and optimized through AI-based automation. Manufacturers of automation systems with AI capabilities have the opportunity to expand their service business in the fields of industry, energy production, and property management. In addition to the mentioned goals, the use of generative artificial intelligence, such as language models, in the development of industrial process applications is also being investigated