Machine learning applied on the district heating and cooling sector: a review

Abstract

Driven by the continuous growing demand for heating and cooling, district heating and cooling systems (DHC) play a major role in the field of energy by providing environmentally friendly solutions for citizens with significant economic impact. Taken also into account the global need for greener and smarter cities, optimization and automation of current DHC operation is more imminent than ever. In order to achieve a transformation of DHC systems, new data-driven technologies are being adopted to reach the goals. In this paper the findings of a systematic literature review are presented covering articles published in the last decades in which the authors described the development and application of machine learning approaches to the DHC sector. In total, 74 articles were retrieved, analysed and categorized into two main categories: (i) heating load/demand prediction and (ii) design, maintenance and scheduling. The survey findings are presented and listed in terms of the machine learning techniques mentioned therein (supervised learning, unsupervised learning and reinforcement learning), the specific application domain (load forecast, design, maintenance and scheduling) of each article providing also insights regarding the source data used and the quality of the results. © 2021, Springer-Verlag GmbH Germany, part of Springer Nature.