# Opportunities for Machine Learning in District Heating
## 6 A Road-Map for Machine Learning and District Heating
### Barriers in the Application of Machine Learning and District Heating  
With increasing digitalisation, decarbonisation, advances in sensor technology, low cost processing and storage in the cloud and advances in ML algorithms, we believe that the stated goals can be achieved. However, a number of specific barriers need to be taken into consideration, which also explain why these goals have not been achieved yet. We have divided the barriers into groups of technical, business and organisational barriers. Technical barriers. The primary technical challenge is the data availability. Successful research in ML depends on having sufficient \"good\" data, i.e., relevant, high quality and at a sufficient resolution of the purpose it is used for. Access to such good data, however, depends to a large degree on a value stream, from: sensor and measurements, data collection and communication, data storage and structure, data analytics, presentation of the analysis to the user and finally the value of the new knowledge to the user. At present day, the ideal value streams hardly exist. It will take years of systematic iterations of improvements to reach \"good\" data. However, ML researchers need to work on demonstrating the benefits that these efforts will ultimately provide--even if this is a long-term strategy. Until this initial work is done, no serious efforts on collecting more comprehensive data will begin in the industry. ML research needs to provide input on the specific needs, and influence the type of data collected by the DH industry. In addition, it is not enough that there are data, but this data must be well-understood and categorised comprehensively. For example, knowledge about what data can be considered to be fault free, versus data that contain operations with faults, is highly necessary. Another example is the ownership and access to the data. This can be exemplified with the \"closed substation\" barrier. We need substations with open communication protocols, so that they can be controlled from the outside with the help of third party solutions. This is necessary to facilitate the district level ML solutions that can be incorporated to each substation. Business barriers. The key in this category are barriers for realising customer incentives. Business models need to create opportunities for tools to incentivise customers to positively impact their energy use behaviour. In general, purely economical and monetary factors are not enough to achieve that, and other solutions are needed, e.g., through visualisation. In addition, energy tariffs rewarding consumers for reducing their return temperature or to offer the flexibility of their building mass for better control can be implemented. The second barrier concerns advancements in the DH industry. There are many obstacles to innovation and change of DH companies and property owners. These industries traditionally focused on exploitation (stable conditions and weak competition) and thus there is little incentive for exploration, to radically develop and improve. Organisational barriers. The most important barriers arise from the DH ownership structure. The distributed aspect makes it difficult to create solutions based on \"big picture\" optimisations. For example, it would be beneficial if network operators own the building substations, so that they can control them directly. Similarly, insufficient customer awareness leads to sub-optimal decisions and configurations. Increasing the awareness of consumers can give them insight into their energy use, for example through gamification or similar tools. Other barriers are due to changing the relation between regulations and fair competition. As previously described, we do see external influences that will change the stable conditions and the mode of exploitation; decarbonisation (change of fuels), electrification (more competition), deregulation (no monopolies). This can drive changed ownership, most likely leading to less public ownership across different parts of the DH industry.