Editorial: Machine learning to support low carbon energy transition

With the accelerated industrialization and urbanization over the past decades and consequent greenhouse gas emissions, climate change has today become a major challenge faced by mankind. To tackle these challenges, a number of countries worldwide have set ambitious targets to reduce carbon emissions substantially in the future (Dong et al.). As the largest source of carbon emissions, the energy sector plays a responsible role in achieving low carbon energy transition (Bamisile et al., 2022).

Given the worldwide background of low carbon energy transition, key carbon capture technologies have been developed rapidly, such as quantitatively evaluating carbon intensity and tracing carbon flow from generation to consumption (Lin and Li, 2022). Moreover, under the increasing penetration of renewable energy sources within power systems, it is urgent to handle the uncertainties caused by intermittent renewable energy output to maintain the reliability and stability of the power grid operations (Hua et al., 2019). Recently, with enabling technologies to achieve decarbonization, machine learning is becoming increasingly important in modelling, planning, operation, and control of the entire energy chain from different energy mixes of electricity, thermal, and natural gas to end-users, e.g., buildings, transportation. Notably, with the digitalization of the energy systems, massive amounts of data from a wide range of sources are available, which makes machine learning applications in the energy industry possible (Hua et al., 2022), including carbon emission estimation, renewable energy output, load forecasting and cybersecurity. With these new developments, it is necessary to integrate these data-driven technologies with model-based methods for achieving the powerful performance to support low carbon energy transition.