Guest Editorial: Special issue on computational methods and artificial intelligence applications in low‐carbon energy systems

Abstract

Climate change has raised serious concerns prompting urgent and broad actions that extend current operation techniques. Computational methods and artificial intelligence have already shown promising results in power systems applications, including analysis, forecasting and equipment inspection. Nevertheless, the urgency required in the clean energy shift will substantially increase operation uncertainty, as well as control and planning complexity. Leveraging the capabilities of faster computation, better accuracy and stronger decision-making from cutting-edge computational methods and artificial intelligence can be a promising approach to avoid most impacts in the clean energy transition, while improving system reliability, economics and sustainability.This special issue is focused on inviting original research, reviews and experimental evaluations to promote new computational methods and artificial intelligence applications in low-carbon energy systems.In this special issue, there are a total of 19 original research articles to present the state-of-the-art in energy forecasting, situational awareness, multi-energy system dispatch and power system operation.On the energy forecasting front:1) The paper entitled "Diffusion-Based Conditional Wind Power Forecasting via Channel Attention" proposes a diffusion and channel attention-based wind power forecasting framework, which transforms wind power data into the frequency domain and applying advanced channel attention techniques [1]. 2) The paper entitled "Short-term Prediction of Behind-the-Meter Photovoltaic (PV) Power Based on Attention-LSTM and Transfer Learning" proposes a Behind-the-Meter PV power generation prediction method based on an attention neural network and transfer learning to separate PV power generation from net load consumption [2]. 3) The paper entitled "Multiple decomposition-aided Long Short-term Memory network for enhanced short-term wind power forecasting" proposes a wind power forecasting method to combine the empirical mode decomposition (EMD) and variational mode decomposition (VMD) to enhance the prediction accuracy of the long short-term memories (LSTM) neural networks [3].