Special issue: AI-Empowered Reliable Forecasting for Energy Sectors

Karar Mahmoud* (Guest editor), Mohamed Abdel-Nasser (Guest editor), Josep M. Guerrero (Guest editor), Naoto Yorino (Guest editor)

*Corresponding author for this work

Research output: Contribution to journalSpecial issueScientificpeer-review

Abstract

AI-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 emphasised that the characteristics of renewable energy sources can provide several obstacles to integrating large-scale renewables in transmission systems and a significant number of dispersed renewables in distribution networks. Besides, electricity demand also has considerable 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 ambition 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. Accordingly, 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.
Original languageEnglish
JournalIET GENERATION TRANSMISSION AND DISTRIBUTION
Publication statusAccepted/In press - 2022
MoE publication typeC2 Edited books

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