# Opportunities for Machine Learning in District Heating
## 3 Method  
With the aim of understanding to what extent current state-of-the-art ML has addressed relevant problems in DH, this research article was developed as follows: (1) workshops to create domain insights, (2) surveys and literature review to refine ideas from those workshops and to establish an inventory of existing knowledge, (3) analysis of information acquired and (4) a road-map proposal building on identified knowledge together with expert opinions. Figure 2 provides a summary of the iterative process and how each iteration was validated. Brainstorming in workshops to better understand the problem area. This article employed brainstorming, a technique that is common in the idea generation phase of several scientific endeavours [22]. Through a series of workshops under the umbrella theme of data analytics for DH, discussions between experts from different R&D projects, as well as DH industrial experts, it became clear that even with a common aim, there were discrepancies in the approaches adopted by most ML experts from the expectations of the DH community, e.g., different expectations on the type and quality of data to be collected from DH networks. The interdisciplinary nature of the workshop participants with different competences exposed the different expectations related to data analytics and DH. 2. Inventory of existing knowledge. Problem areas identified in step 1, above, were further expanded though a review of relevant literature in the area, but also through consultative discussions with DH experts. For instance, we draw inspiration from work done in the _Digital road-map for district heating and cooling_ ([[https://www.euroheat](https://www.euroheat)]([https://www.euroheat](https://www.euroheat)). Figure 2: Method outline indicating iterations through which the article was developed and how each iteration was validated. Each iteration involved closed collaboration between DH and ML researchers. org/wpp-content/uploads/2018/05/Digital-Roadmap_final.pdf accessed on 15 May 2021) to identify interesting problem areas in DH. To understand how ML research has developed in the area of DH, a state-of-the-art literature review was completed [23]. To identify the most relevant research, a search was conducted using the phrase \"Machine Learning and District Heating\" with the help of Harzing's Publish or Perish software [24]. In line with the clear trend of recent growth in the field, the search was limited to the last 10 years (2010 to 2021) during which most of the research in the area has been reported (see Figure 3). Initially, 1000 articles were selected. These were pre-screened by reading through the title and in some cases the abstract of each article when the title was unclear. We cross-validated against each others perception to minimise the selection bias. The result was a selection of 179 relevant articles. By relying on a meta-search resource such as Harzing's Publish or Perish, the most relevant research articles could be indexed from a variety of databases. The search was limited to \"Machine Learning and District Heating\" because the authors came to the conclusion that no relevant article will exclude such keywords even if some may also include Artificial Intelligence. 3. An analysis of existing knowledge to determine the extent to which various DH relevant problems have been addressed by ML research. Other literature reviews were analysed to determine whether they provided enough of an overview of how ML has been applied in the field of DH. However, we perceived that the identified review studies left room for focusing on the discrepancies between relevant industrial challenges in DH and the state-of-the-art applications of ML. Still, they were invaluable in shaping our understanding of the current state-of-the-art in ML applications and district heating. Relevant information was extracted from the selected articles in a way that allowed for identification of major themes in terms of the problem area addressed.