# Opportunities for Machine Learning in District Heating
## 4 Machine Learning Applied to District Heating
### Highlights of Existing Literature Reviews  
To understand existing literature reviews in this area, we provide an overview of the knowledge gaps identified in various studies, the types of ML models reported, datasets employed and building characteristics (features) studied. Most research articles on the application of ML in DH have occurred in the last 10 years [7; 15; 18]. Each of the review studies addressed different research gaps, considered research articles over different time periods and considered various building types. Some choose to cover the full breadth of ML methods, while others limit the scope of the ML models included [7; 26]. Knowledge gaps addressed by previous review studies include: (1) lack of an overview of building performance models, in general [7; 26], and the lack of data driven approaches for building performance, in particular [15; 25], (2) lack of comprehensive studies of ML in urban building energy performance [7], (3) lack of ML performance and accuracy assessment on DH problems [16] and (4) lack of open datasets for benchmarking algorithms [15]. Other review studies highlight the need to compare the forecasting performance of common ML algorithms such as Artificial Neural Networks (ANN), Support Vector Machine (SVM) or Gaussian-Based Regression (GBR) [7; 18]. Another gap is the lack of discussion of the key strengths and weaknesses of \"white-box\" models, \"black-box\" models, but also hybrid models, with focus on building modelling and energy performance prediction [26]. Many review studies have synthesised and reported on the different types of ML models applied in the energy sector as a whole. Examples include a study of the application of ANN, SVM, GBR and clustering methods when analysing building energy performance as a whole [8]. A similar review [7] came up with the conclusion that ANN, Support Vector Regression (SVR), Genetic Algorithm (GA) and Random Forest (RF) were common methods applied to individual buildings. Reviews have also focused on specific methods, such as deep learning [18] and ANN [17]. To improve prediction accuracy, there is increasing interest in using hybrid methods, e.g., ANN and SVM [8; 15]. None of the reviews found a single overall best model although SVM was reported to perform relatively better compared to ANN and regression-based methods, yet the most used method for building energy prediction remains ANN [16]. Review studies have found no evidence to suggest that a given ML model is superior for a specific problem domain, but rather, the suitability of every model is determined by the choice of features and size of data [8]. Feature selection is difficult because the desired features rarely align with available data. For example, most ML forecasting models do not incorporate building space functionality, occupancy and humidity, due to lack of data even though this could be a useful feature [7; 16]. As a result of the type of data and models used, predictions are often limited to short term [16; 25]. In general, reviews have found it difficult to establish an overview of the dataset employed due to a lack of detailed information about the dataset from various studies. In addition, there is lack of testbeds and automated and streamlined data-processing for building energy prediction [15]. Model accuracy has been a difficult topic in the reviews due to the lack of a common set of criteria through which ML models can be compared in the domain of building energy forecasting [7]. Still, many articles make attempts to validate their findings by comparing performance with other algorithms on similar tasks [15; 17]. Following the analysis of the scope of various review articles, we can now state that ML research for DH is dominated by energy demand forecasting [7; 8; 15; 19; 25]. Following the analysis of the scope of various articles, we do not know of any review that has looked into the relevance of ML research problems from the perspective of the DH industry.