# Opportunities for Machine Learning in District Heating
## 6 A Road-Map for Machine Learning and District Heating
### Suggestions for Different Stakeholders  
The current situation affects different stakeholders in different ways, and gives them different opportunities for advancement. In this section, we present selected suggestions for each of the five stakeholder groups: DH companies, DH customers (primarily building owners), the research community, technology suppliers and consultants and policy makers at local, national and international levels.  
In terms of \"priority of problems\" from the industry, it is important to notice that there is not one \"industry\" to begin with. What is important to research or develop depends on whether one is selling to, or collaborating with, the energy companies (production or distribution), maintenance firms, building owners, etc. Additionally, DH networks are not operating in a vacuum. Other solutions, such as heat pumps and ventilation systems, must be taken into account. Much of the DH industry is still somewhat catching up with the introduction of heat pumps, which has, in some cases, benefited from using the heat pump within its optimal range of operation, and only tapping into district heating during peak demand periods when the heat pump capacity does not suffice any more. That mode of operation is very clearly economically infeasible for DH operators. How to regulate these different solutions is an open question, and it is handled very differently in different countries. Condition monitoring and predictive maintenance will be gaining prominence, especially techniques that do not look at one substation in isolation, but can analyse the complete network [46]. It is crucially important in particular for low temperature networks, which require correctly behaving substations to achieve the desired effect. To a large extent, it is still an open area for research.  
Innovations and improvements would also benefit from a more openness among DH companies to communicate their challenges and problems. The DH companies and their customers (property owners) need to better understand the possibilities that come with digitalisation and machine learning, the opportunities for increasing efficiency and generating new revenues (digital service design). If DH companies and the building industry communicate their challenges better, and the research community understands this context better, the impact of the research will increase.  
On the other hand, both the DH companies and their primary customers must put some effort into better understanding the possibilities that come with new technologies (digitalisation and machine learning). The opportunities for increasing efficiency and generating new revenues (digital service design) are vast, but ultimately, nobody else can do this work. To survive long-term, they must become aware of the problems and challenges that will increase in importance in the future. An example direction is working on better alignment of usefulness and value to the user--there is a lot of talk in DH companies about customer engagement and dialog; however, very few concrete ideas for useful services have been proposed in the literature or implemented in practice.  
To understand the real problems and to develop solutions, the researchers must form cross-disciplinary teams. There is also a paradigm shift in research methods. The traditional researcher (model development) in energy systems as well as in data science could perform good research from a desktop, on his own. The new research in the era of digitalisation and ML must be aligned with real data which consequently requires deep interaction with many actors, and with technology suppliers with the required knowledge and capabilities.  
Policies, such as taxes, incentives and regulations on local, national and EU levels also have a potential to be smarter, focusing more on effectiveness, rather than blunt focus on energy volumes without considering time and place.