CALL FOR SUBMISSIONS: Advanced Solar Energy Forecasting and Management Systems for Grid Optimization (SI-ASEM)

2025-04-12T12:37:13+00:00

	Introduction: Physical solar predicting is a predictive method that uses imagery from satellites, sky images, and numerical weather prediction (NWP) models to simulate the movement of sunlight, clouds, and the atmosphere in order to estimate solar power generation. This allows for more accurate forecasts of photovoltaic energy output based on the physical characteristics of the environment. Persistence forecasting is one technique used by statistical approaches that uses generating data from both the past and present to statistically modify predictions made by numerical weather prediction models. A simple statistical methodology known as persistence forecasting makes the assumption that the current amounts of solar power will continue in the near future. It is used as a benchmark for assessing more complex forecasting techniques. All generators, grid operators, end users, and other stakeholders in the electricity market have their needs and capabilities coordinated by smart grids, which maximizes system resilience, flexibility, and stability while minimizing costs and environmental effects. When it comes to reliability and resilience, smart grids outperform traditional grids. Smart grids with self-healing capabilities can identify and isolate faults automatically, rerouting power to reduce interruptions and downtime. On-grid and off-grid solar systems are the two primary categories. On-grid solutions add electricity to what you get from the utility company by connecting to the grid. Off-grid solutions, on the other hand, depend on battery storage and are completely autonomous. They have similar advantages in spite of their disparities. The general terms under which installations link to the high- and extra-high-voltage grids, or the low-voltage and distribution grids, respectively, are governed by the grid code. In addition, system services, grid growth, and general grid operation are covered in the code. An on-grid solar system offers numerous benefits. It can, first of all, lower the power costs. They can reduce the cost of the electricity you use from the grid by selling any extra energy the system produces back to the grid. Additionally, it can lessen your carbon footprint. The main elements of a smart grid are covered in the paper, including demand side management, distribution systems, smart device interfaces, transmission systems, storage, and monitoring and control technologies. The growth of smart grids requires that suppliers and customers be able to communicate information and power, given the significant advancements in intelligent communication, monitoring, and management systems. At least in the regions covered by the PEV owner's energy supplier, smart grid technologies present a viable remedy for this issue. When PEVs are connected to a charging station, they may recognize themselves thanks to the Smart Grid, and the electricity consumed can be immediately charged to the owner's account. An installation that is not connected to the electrical grid is referred to as an off-grid PV system. This indicates that all of the energy generated is used and stored locally. Off-grid PV systems essentially run on the energy that is kept in a battery bank. Power plants, electrical substations for stepping up or down voltage, long-distance electric power transmission, and lastly electric power distribution to consumers make up an electrical grid. We encourage submissions from a variety of fields and viewpoints, such as but not limited to: Advanced Solar Energy Forecasting and Management Systems for Grid Optimization. List of Topics: Forecasting solar and photovoltaic power for smart grid energy management. AI-powered forecasting for efficient smart grid operation and optimal solar energy management. New AI-powered energy management system integrated with RES for smart grids. Advanced forecasting models for the integration of PV energy into smart grids. Day-ahead energy forecasting in a smart grid that takes demand response and microgrids into account for efficient energy optimization. Facilitating distributed grid optimization and renewable energy system solutions. A review and assessment of the most recent developments in PV solar power forecasting. AI-powered metaheuristic optimization for smart grids: predicting and managing the output of renewable energy. Deep Learning Model on Energy Management in Grid‐Connected Solar Systems. Advanced optimizer for tracking solar systems' highest power point in a smart grid. Forecasting energy loads in smart energy management networks using computational intelligence techniques. Techniques for grid-connected energy storage system optimization and energy management.
	Deadlines for this the Special Issue: SI-ASEM
	Manuscript Submission Deadline	25/ 12/ 2025
	Authors Notification	15/ 02/ 2026
	Revised Papers Due	22/ 04/ 2026
	Final notification	10/ 06/ 2026

	Guest Editor Information:
	Lead Guest Editor (LGE) (List order #1)
	Title: Dr.
	First name: Linh H.
	Last name: Tran
	Job title: Electrical Engineering
	Organization: Hanoi University of Science and Technology, Vietnam
	Institutional e-mail address: prof.linhtran@gmail.com, linh.tranhoai@hust.edu.vn
	ID (ORCID, Scopus, etc. ): https://orcid.org/0000-0001-9757-8041
	Area(s) of Expertise (3-5 keywords): Artificial Intelligence, Neural Networks, Fuzzy Logics, Big Data, IoT.
	Guest Editor (GE) (List order #2)
	Title: Dr.
	First name: Adedayo Ademola
	Last name: Yusuff
	Job title: College of Science, Engineering & Technology
	Organization: University of South Africa, Pretoria, South Africa.
	Institutional e-mail address: yusufaa@unisa.ac.za

Guest Editor (GE) (List order #3)

Title: Dr.

First name: Abulwafa

Last name: Muhammad

Job title: Computer Science Faculty

Organization: Universitas Putra Indonesia YPTK Padang, Indonesia

Institutional e-mail address: abulwafa@upiyptk.ac.id

ID (ORCID, Scopus, etc. ): https://orcid.org/0000-0003-2512-140X

Solar Energy and Sustainable Development Journal: Announcements

CALL FOR SUBMISSIONS: Advanced Solar Energy Forecasting and Management Systems for Grid Optimization (SI-ASEM)