# Opportunities for Machine Learning in District Heating
## 5 Gaps between Industrial District Heating Needs and Current Machine Learning Research
### Control and Optimisation  
There are convincing indicators (following research and practice) that the single most useful adaptation of current ML research would be to consider the control and modellingproblems together. One of the main reasons is because control strategies open the doors to supervised ML and enable interpretation of results for unsupervised ML, e.g., anomaly detection. It brings certain challenges, of course, not the least of which is the lack of data related to the network control (e.g., network structure and current parameters) and also optimisation criteria. It could be interesting to consider the comparison between model predictive control systems (that can be built on top of the proposed ML forecasting methods) against existing classical control systems.  
The benefits of linear versus non-linear or deterministic versus stochastic models should be evaluated, as well as the robustness of different solutions.  
The understanding of a trade-off between building a simplified model and solving the optimisation exactly, versus building a more complex (but also more accurate) model and only obtaining approximate solution, is lacking.  
The next step, then, is the optimising operation of the entire DH network on a global scale, where the complexity and volatility clearly necessitates employing ML-based tools. This requires, first, a reliable model of the network, including the pipes, the substations, customers, heat exchanging equipment, etc. One can differentiate between networks with constant supply temperature throughout the year, and those where supply temperature changes throughout the year. Depending on the existing customers and the equipment base, one might compare variable flow (with constant temperature) to a constant flow (with variable temperature) and find that one is better suited than the other, given specific local circumstances. Generally, there is a need to build a network model that is a simplified version of reality. Realistically, there is no benefit of modelling every pipe and every substation. One needs to identify \"critical objects\" spread throughout the network. Assuming an existing network and including demand-side management already leads to quite complex optimisation. One can next, of course, also imagine optimising the planning, development and building of the DH network itself.  
Another aspect, seldom addressed in the literature but which constitutes a challenge in the industry, is how to configure and manage a large heterogeneous system. In the framework of model predictive control (MPC), it is certainly possible to build one model per building, however, that comes with each one having their own choice of parameters for how to solve the corresponding optimisation problem outside the actual mathematical formulation, e.g., regularisation, scale factors, error tolerance, number of iterations, etc. Making these kinds of adaptations at scale is challenging. A well-informed and interested engineer can make a reasonable choice for any given substation; however, it is not possible for the whole network consisting of thousands of substations, most with only somewhat reliable data.  
As with any other industrial process, the efficiency of the heat generation process itself is of interest. However, we expect that any such improvements will have to be tailored to the particulars of the energy plant in question and are better addressed as such, rather than in the framework of heating networks, which is about the optimal use of the heat.