Deep Learning for Real-Time Prediction and Optimization in Renewable Energy Systems

Deep Learning for Real-Time Prediction and Optimization in Renewable Energy Systems

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. This technique seeks to lower
energy usage. Optimization of Energy Systems demonstrates in detail how to model, analyse, and
optimise thermostatic energy systems of many kinds for a range of applications.
Productivity and optimization of energy are important factors in climate change mitigation. Emissions
of greenhouse gases are greatly decreased by improving energy consumption across multiple
industries, which encourages a sustainable future. An essential component of a project developer's
work involves managing the delivery and status of applications with the administrative and local
authorities, as well as organising and supervising all the research necessary to secure administrator
licence. A project developer is also in charge of risk management and project planning. 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. Energy
storage technology will advance rapidly, improving grid stability and facilitating a more seamless
integration of renewable energy sources into current networks.
The adoption of energy storage will be supported by advancements in battery technologies, such as
longer lifespans, quicker charging times, and lower costs. It offers a fresh perspective on the system
and the procedure for establishing appropriate objective functions in order to identify the best design
parameters for obtaining increased sustainability, cost effectiveness, and efficiency. It entails locating
and fixing flaws, inefficiencies, and bottlenecks that compromise the system's usability and
functionality. Enhanced overall performance is one of the most significant advantages of system
optimization. One may save time and money by making sure that it operates faster and more
efficiently by optimising its configuration. Additionally, system optimization helps protect the
machine from malware and viruses. The hazards of keeping a flawed procedure unaltered can be
decreased by effectively employing a method. This makes it easier for businesses to see new possible
hazards and get rid of them while they have a significant negative impact on the project or workflow.
In project management, process optimization is crucial for streamlining processes and optimising
resource use. Errors are decreased, waste is decreased, and overall output is improved. 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.

Timeline:
Last Date for Manuscript Submission 03.03.2025
Notification to Authors 16.05.2025
Revised Manuscript Due 24.08.2025
Decision Notification 28.10.2025

Guest Editor Details:
Dr.Thomas O Olwal

Professor,
Department of Electrical Engineering, Tshwane University of Technology, University, Pretoria, South Africa, OlwalTO@tut.ac.za 

Prof. Karim Djouani

LISSI Laboratory, Université Paris-Est Créteil, 94000 Créteil, France, djouani@u-pec.fr

Dr. Olumide Alamu

Department of Electrical Engineering, University of Lagos, Nigeria, oaalamu@unilag.edu.ng