AI for Predictive Maintenance in Commercial Refrigeration Unit Systems

PreMaCOOL focuses on the development of an AI (Artificial Intelligence) model that will serve as a predictive maintenance tool for cooling systems.

Klimamichaniki has been operating in the HVAC and energy domains of Greece since 1984. Continuously striving to stay ahead of the competition, Klimamichaniki has been innovating every step of the way. Klimamichaniki’s latest endeavour is to incorporate the power of data and data-driven methodologies in order to develop custom solutions, offer advanced insights to its customers, and attract potential clientele.


Development of machine learning models is not an easy task for non-experts. Klimamichaniki has vast experience in the energy and HVAC domain, but is new to implementing machine learning and artificial intelligence algorithms. Collecting data from an installation is the first of many steps that takes to create an AI model, and Klimamichaniki is looking for partners and consortia with expertise on the matter, in order to be able to develop robust solutions. Additionally, given that the scope of the pilot is to create a predictive maintenance model, a significant challenge will be the absence of failure events for the cooling units within the duration of data collection. This challenge will be overcome by utilising other datasets, in order to understand the mechanics behind predictive maintenance, and possibly utilise this information for transfer learning purposes.


Work Plan

PreMaCOOL is based on a linear workflow, based on sequential and parallel activities. On the technical aspect, PreMaCOOL will be based on the following activities:



  • Setting a communication mechanism in order to retrieve data
  • Align all dataset variables based on rounded timestamps
  • Investigate for anomalies and gaps, and clean the dataset
  • Perform normalisation when necessary
  • Restructure data for proper training



  • Investigate algorithms
  • Train model
  • Fine tune selected model
  • Select performance metrics


In parallel, PreMaCOOL’s dissemination activities will be taking place throughout the duration of the pilot. Social media posts, website announcements and other forms of communication will be part of the pilot in order to disseminate the company’s research activities. Direct communication, still a prominent means within Klimamichaniki’s network, will also take place, in order to show the company’s new profile, as well as to understand the clients’ interest and intent, to utilise such a service.

By the end of the pilot, Klimamichaniki will have valuable results to demonstrate, and a clear path on how to proceed with the commercialization of such a service.

Business Partner

EOSC Service provider

Supporting project