Joint human-machine learning can improve district heating

Many industries today produce and save large amounts of data from their operations. By using data driven models, the operations can continuously be monitored and adjusted for maximum efficiency. Ece Calikus is a PhD student working with research and development of so called self-monitoring systems that uses artificial intelligence, more specifically machine learning, in combination with human knowledge for industrial applications.

“The ability to diagnose deviations and predict faults effectively is an important task in various industrial domains for minimising costs and productivity loss and also conserving environmental resources”, says Ece Calikus, PhD student at Halmstad University.

An intelligent and self-monitoring system is when industrial equipment monitors its own operation. It can learn over time and automatically identify problems or deviations. There is however always a need for human interpretation and evaluation of these systems. Human experts can add other aspects, for example business and societal aspects, to achieve a holistic improvement of the industry's operations.

Humans and machines work better together

The combination of machine learning models and human expertise is called joint human-machine learning.

“The goal with joint human-machine learning is to create a collaborative process where machines and humans learn from each other. People should be able to observe, interpret and learn from the self-monitoring results, as well as provide domain knowledge or feedback to the intelligent system”, says Ece Calikus who published her licentiate thesis on the subject in 2020.

Ece Calikus is a PhD student at the School of Information Technology at Halmstad University. She is working at the Center for Applied Intelligent Systems Research (CAISR) in a research project called Self-Monitoring for Innovation (SeMI). Her licentiate thesis focuses on generating a real-world self-monitoring system that can learn data and their relations over time and detect anomalies and discover faults using joint-human machine learning.

“Throughout the thesis, I have described a number of different approaches, each designed for the needs of a self-monitoring system, and I have composed these methods into a coherent framework”, says Ece Calikus.

Improving district heating by automatic detection of faults

Self-monitoring systems can be applied to a range of different industries. Ece Calikus is focusing her research on applying these intelligent systems to district heating in order to increase the efficiency in that domain. The research is done in collaboration with two district heating companies, HEM and Öresundskraft. They have provided both data used in the research and knowledge about district heating.

“When applied to district heating, the self-monitoring system can automatically detect faults and abnormal patterns in district heating networks by comparing heat demands among different groups of customers”, says Ece Calikus.

When faults and deviations are detected in time, the districts heating companies can use this information for further investigation, to repair faults and optimise their operations.

“Future energy systems are facing critical challenges such as the steady growth of energy demand, energy resource depletion, and increasing emissions of carbon dioxide and other greenhouse gases. District heating can play a vital role in the implementation of future sustainable energy systems that will contribute to a decrease in carbon emissions. However, the current generation district heating technologies have problems that prevent them to operate efficiently. I hope that my research can lead to more efficient district heating and thus contribute to a more sustainable energy supply system”, says Ece Calikus.