# Opportunities for Machine Learning in District Heating
###### Abstract  
The district heating (DH) industry is facing an important transformation towards more efficient networks that utilise significantly lower water temperatures to distribute the heat. This change requires taking advantage of new technologies, and Machine Learning (ML) is a popular direction. In the last decade, we have witnessed an extreme growth in the number of published research papers that focus on applying ML techniques to the DH domain. However, based on our experience in the field, and an extensive review of the state-of-the-art, we perceive a mismatch between the most popular research directions, such as forecasting, and the challenges faced by the DH industry. In this work, we present our findings, explain and demonstrate the key gaps between the two communities and suggest a road-map ahead towards increasing the impact of ML research in the DH industry. Machine Learning; district heating; review; road-map; research opportunities +
Footnote †: journal:  
ML focuses on computational techniques for learning intrinsic relationships, from structured and unstructured data, resulting in models that generalise these relationships for the purpose of analysing new but previously unseen data. The value created by data-intensive methods such as ML inherently depends on the quality of data employed and the data collection cost. While data collection has traditionally been a costly endeavour, developments in sensor technology and networking have lowered such costs with the help of technology such as Internet of Things (IoT) [9]. Data about utilities, such as water, electricity and heating, although often collected for billing, can and are being employed to create additional value, e.g., detecting deviating and sub-optimal behaviours of DH substations [10]. District heating is a technology for distributing heat generated in a centralised location, through a system of insulated pipes, for residential and commercial heating requirements, such as space heating and domestic hot water production. DH has been used in a large scale for more than 100 years [11]. The oldest generation of district heating (1 st Generation District Heating or 1 GDH) made use of steam distribution systems. The second generation, 2 GDH, distributes pressurised hot water (between 80 \({}^{\circ}\)C and 150 \({}^{\circ}\)C) in hollow concrete ducts and is common in many cities in Europe. In the third generation, 3 GDH, pre-insulated pipes, buried in the ground, were developed. Pre-assembled substations were introduced, gradually with more advanced control and automation. 4 GDH [12] is distributing hot water at minimum temperature levels, usually around 60 \({}^{\circ}\)C (minimum level to prevent spread of legionella bacteria). Currently, ambient loops are being investigated, with neutral distribution temperatures (15-60 \({}^{\circ}\)C) in combination with decentralised heat pumps to boost the temperature in buildings. This concept is sometimes referred to as 5 GDH [4]. Although today all generations of DH are still in operation, most existing DH systems have undergone transformations in the form of generational adaptations throughout their lifetimes. New advances in technology have characterised the transition between these generations. For example, the 3G to 4G transition was enabled by an information and communication technology layer and associated smart energy infrastructure ([[https://www.energyville.be/en/press/expert-talk-digital-district-heating-networks-why-digital](https://www.energyville.be/en/press/expert-talk-digital-district-heating-networks-why-digital)]([https://www.energyville.be/en/press/expert-talk-digital-district-heating-networks-why-digital](https://www.energyville.be/en/press/expert-talk-digital-district-heating-networks-why-digital)))(accessed on 15 May 2021). In addition, each transformation stage has consistently lowered temperature levels in the networks. As a result, the heat loss is consistently reduced, and new opportunities are created for integrating many low-temperature sustainable energy heat sources.