2.2.1. Automated Machine Learning

Successful application of machine learning is critically dependent on the choices made before the learning algorithm is executed. These include the specific algorithm to use for a given problem, how to pre-process the features in the dataset, and how to set the hyperparameters, i.e., the non-optimised configuration of the chosen algorithm. Finding a successful framework is often an iterative and time-consuming process, requiring the training and evaluation of many different algorithms and hyperparameters, which may make the technology inaccessible for non-specialists. These difficulties have led to the development of automated machine learning that typically utilises Bayesian optimisation to search the space of frameworks with the aim of producing an optimised model for the task at hand [19,20]. This simplifies the machine learning workflow and allows the evaluation of a range of proven techniques and implementations for a given problem. This approach has great potential in broadening the use of machine learning and allowing non-specialists in fields, such as energy, to make use of the technology. In this work, two different implementations of this technique were explored:


The results produced by both implementations were not significantly different and, therefore, only one was reported on in this paper. The AdaNet model was chosen as it provided easier access to probabilistic outputs, which were used in the subsequent analysis.
