Transforming our energy systems to make them free of carbon emissions poses technological and scientific challenges. These hinge on aspects of production of electricity and its delivery to end users, consumption patterns and future scenarios, and electrification of transportation and heating. As well as socio-economic and environmental issues that need to be addressed for a rapid and sustainable transition. Many important problems in these areas are currently being studied across technical, social and environmental sciences. The application of machine learning (ML) and artificial intelligence (AI) spans these disciplines, offering powerful tools and methods.
Relevant topics include the demonstration of smart grid technologies by means of application cases and experiments that illustrate current consumption patterns and their spatial distribution. Modelling of production and consumption with future energy pricing concepts. Closer to the power plant and electric grid operators, themes such as the modelling of energy production at different time scales, and the optimization of system operation and life cycle. All of these topics are currently experiencing a large flow of data from smart meters, environmental monitoring systems, renewal energy power plants, electricity market, electrical grid monitoring systems, as well as physics based models. Thus, novel applications of state-of-the-art methods for supervised and unsupervised classification, time series modelling and forecasting, computer vision, optimization with feedback from data are expected. For the sake of strengthening analyses and results from new methods, comparisons are encouraged. Similarly, aspects of data and model selection, and hyperparameter tuning.
Transforming the way we produce and consume energy to reduce our environmental footprint, requires also social and economic aspects to be taken into account. Such aspects of the energy transition also benefit from a large influx of data. Thus, themes such as environmental benefits of electrification, management of energy needs at the city, cantonal, national or even inter-country level, and analyses and modelling of environmental data are important in this track. Because they can yield new valuable feedback to policy makers. They may also include a diversity of heterogeneous data, including time series, images, and unstructured data.