The journal Computers & Electrical Engineering provides rapid publication of topical research into the integration of computer technology and computational techniques with electrical and communication and information systems. The journal publishes papers featuring novel implementations of computers and computational techniques in areas like:

Signal Processing
Power Engineering (including renewable and green energies)
Artificial Intelligence - methods and applications
Security
Privacy
Communication

The journal regularly publishes special sections covering specific topics of interest. Proposals for special sections should be submitted to the Editor-in-Chief. The list of current special sections can be found at https://www.sciencedirect.com/journal/computers-and-electrical-engineering/special-issues.

Special Issue on Deep Learning for Real-Time Prediction and Optimization in Renewable Energy Systems
截稿日期: 2026-05-16

Renewable energy system improvement is an infrastructure for energy strategy that assists clients achieve their energy efficiency and saving money objectives by providing simultaneous, multi-technology integration and evaluation options. Electricity production, water and heating and cooling of spaces, and transportation can all be accomplished with energy from renewable sources. In contrast, energy that is not renewable is derived from limited resources like coal, oil, and natural gas.

Along with many other advantages including enhancing air quality, safeguarding the environment, and enhancing energy security, optimising energy use can result in significant cost savings. By modifying the power injection to be perpendicular to the load current, energy optimization increases ride-through capability without compromising energy storage capacity. Emissions of greenhouse gases are greatly decreased by improving energy consumption across multiple industries, which encourages a sustainable future. The concept of renewable energy application emphasises the use of dual fuels under a variety of circumstances to maximise the potential of renewable energy sources, such as LPG, natural gas, and hydrogen, particularly in low load situations to efficiently handle the unpredictability of these sources. To precisely forecast wind and solar energy production, renewable system operators combine weather data, satellite data, statistical techniques, and numerical models for weather prediction. When historical and real-time data are accessible, forecasting methods become more accurate. A variety of fields and viewpoints are welcome to contribute, including but not limited to: Deep Learning for Real-Time Prediction and Optimization in Renewable Energy Systems.

The following topic of interest can be included but not restricted:

Deep Learning Networks for Real-Time Solar Power Prediction.​
Forecasting Wind Power with Real-Time Deep Learning Techniques.
Deep Learning-Based Energy Storage System Optimization in Renewable Energy Networks.
Deep Learning-Based Fault Detection and Predictive Maintenance for Solar and Wind Farms.
Deep Neural Networks for Short-Term Load Forecasting in Renewable Energy-Powered Smart Grids.
Combining Solar and Wind Power Prediction with Hybrid Deep Learning Models.
Optimising Energy Demand Response in Renewable-Integrated Systems using Deep Reinforcement Learning.
Autoencoders and GANs for the Identification of Anomalies in Renewable Energy Systems.
Energy Pricing Optimization for Renewable Power Markets Using Deep Learning.
Using Transfer Learning to Forecast Solar Power Generation under Cloudy Circumstances.
Optimising Smart Grids in Renewable Networks using Deep Learning-Driven Control Mechanisms.
Optimisation of Renewable Energy-Powered Electric Vehicle Charging Stations in Real Time.
Deep Learning-Based Power Quality Improvement in Renewable Energy Systems approach.

Guest editors:

Thomas O Olwal
Tshwane University of Technology, Pretoria, South Africa

Karim Djouani
University Paris-Est Créteil Val de Marne, Créteil, France

Olumide Alamu
University of Lagos, Yaba, Nigeria

Manuscript submission information:

Manuscript submission deadline: 16 May 2025

You are invited to submit your manuscript at any time before the submission deadline. For any inquiries about the appropriateness of contribution topics, please contact the Guest Editors.

Please refer to the Guide for Authors to prepare your manuscript, and select the article type of “VSI: DL for Real-Time Prediction and Opt in RES” when submitting your manuscript online at the journal’s submission platform Editorial Manager®. Both the Guide for Authors and the submission portal could also be found on the Journal Homepage.

Keywords:

Deep Learning; Machine Learning; Artificial Intelligence; Real-Time Prediction; Optimisation; Renewable Energy Systems; Smart Grids; Predictive Maintenance

Special Issue on Recent Advances in Multimedia and Multimodal Data Security
截稿日期: 2026-05-31

With the development of Internet technologies, large volumes of multimedia and more generally, multimodal data, can easily be shared among different users or entities for the purpose of diagnosis, analysis, search and recommendation, prediction etc. in a variety of critical application contexts with no hard-copy backups being kept. Due to new development in science and technology, different methods are used to copy, recreate, distribute and store these contents easily for many real-world applications. However, various criminal offences such as identity theft, copyright violation, misuse of personal and medical information, authenticity and confidentiality is being made every day in our daily life and greatly impact on financial loss. Malicious actors can extract sensitive information or manipulate outcomes. These challenges are particularly critical in multimedia and multimodal environments, where the convergence of different data types—such as images, audio, and video—presents unique opportunities and risks. Applications span diverse domains, including smart healthcare, intelligent transportation, industrial automation, robotics, media and entertainment, legal proceedings, news, insurance, and business sectors. Therefore, addressing these challenges has been an interesting problem for researchers in the field. Also, by using techniques such as data augmentation and generative adversarial networks, we can automatically generate large-scale multimedia and multimodal data to train the AI models. However, new attacks are still being developed and the protection of the multimedia systems from these attacks will continue to be crucial. Motivated by these facts, this special issue targets the researchers from both academia and industrial to explore and share new ideas, approaches, theories and practices with focus on multimedia and multimodal data security and privacy solutions for real-world applications.

Guest editors:

Amit Kumar Singh
National Institute of Technology Patna, Patna, India

Stefano Berretti
University of Florence, Florence, Italy

Manuscript submission information:

Manuscript submission open date: December 31, 2025
Manuscript submission deadline: May 31, 2026

You are invited to submit your manuscript at any time before the submission deadline. For any inquiries about the appropriateness of contribution topics, please contact the Guest Editors.

Please refer to the Guide for Authors to prepare your manuscript, and select the article type of “VSI: Multimodal Data Security” when submitting your manuscript online at the journal’s submission platform Editorial Manager®. Both the Guide for Authors and the submission portal could also be found on the Journal Homepage.

Keywords:

Multimedia; Multimodal; Data Hiding; Encryption; Security; Privacy; Blockchain; Cybersecurity; Social Media; Biometrics; Attacks; Forensics; Deepfake