Guest Editorial: Artificial intelligence-empowered reliable forecasting for energy sectors

Recently, there has been a dramatic increase in the deployment of diverse types of intermittent renewable energy sources (RES), leading to significant energy supply variability. It should be emphasized that the characteristics of RES can provide several obstacles to integrating large-scale renewables in transmission systems (TS) and a significant number of dispersed renewables in distribution networks. Besides, electricity demand also has a considerably fluctuating nature, which is expected to be more challenging with the continued electrification of energy demand for heating and transport, besides the power-to-gas coupling. Accordingly, this is a global trend towards coupling energy sectors to provide more flexibility and regularity options.

In this context, reliable forecasting is an essential tool for system operators to ensure the safe and optimal operation of the energy sectors. This ambitious target can be achieved by improving the dependability and precision of forecasting methodologies required while considering data uncertainty. In this regard, Artificial Intelligence (AI) and machine learning have shown powerful prediction capabilities. This Special Issue intends to cover the most recent advances in the forecasting task in energy sectors (generation, demand, energy prices etc.) through the empowerment of AI.