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Dr. Jing Qiu
School of Electrical and Computer Engineering, University of Sydney, Sydney, Australia
College of Science and Engineering, James Cook University, Townsville, QLD 4810, Australia
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Artificial Intelligence and Deep Learning for Energy Systems and Power Systems

Abstract submission deadline
30 June 2027
Manuscript submission deadline
31 August 2027
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406

Topic Information

Dear Colleagues,

The rapid digitalization and decarbonization of energy infrastructure have created strong demand for intelligent, data-driven methods to improve the planning, operation, control, and market performance of modern energy and power systems. This Topic aims to collect high-quality studies on the application of artificial intelligence (AI) and deep learning techniques to power system analysis, renewable energy integration, energy management, electricity markets, and grid resilience. We welcome original research and review articles covering, but not limited to, load and price forecasting, renewable generation prediction, fault diagnosis, condition monitoring, demand response, optimization-assisted AI, reinforcement learning for control, multi-agent coordination, and AI-enabled decision support for low-carbon energy systems. Contributions that combine physics-based models and data-driven methods, or address practical deployment issues such as uncertainty, interpretability, robustness, and scalability, are especially encouraged. This Topic seeks to provide a timely platform for researchers and practitioners to share advances that support reliable, economical, and sustainable energy and power systems.

Dr. Jing Qiu
Dr. Jiajia Yang
Topic Editors

Keywords

  • artificial intelligence
  • deep learning
  • power systems
  • energy systems
  • renewable energy forecasting
  • electricity markets
  • smart grids
  • reinforcement learning
  • energy management
  • grid resilience

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
AI
ai 		5.0 6.9 2020 19.2 Days CHF 1800 Submit
Applied Sciences
applsci 		2.5 5.5 2011 16 Days CHF 2400 Submit
Energies
energies 		3.2 7.3 2008 16.8 Days CHF 2600 Submit
Processes
processes 		2.8 5.5 2013 14.9 Days CHF 2400 Submit
Sci
sci 		- 5.2 2019 26.7 Days CHF 1400 Submit
Sustainability
sustainability 		3.3 7.7 2009 17.9 Days CHF 2400 Submit

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Published Papers (1 paper)

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Article
Predictive Energy and Exergy Assessment of Photovoltaic Systems Under Dynamic Environmental Conditions Using Machine Learning
by Gökhan Şahin and Erdal Akin
Appl. Sci. 2026, 16(10), 5049; https://doi.org/10.3390/app16105049 - 19 May 2026
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Abstract
This study evaluates the performance of a commercial silicon-based photovoltaic (PV) module under varying environmental conditions, including solar irradiance, module and ambient temperatures, humidity, and wind speed. Key performance indicators such as daily and lifetime energy output, CO2 reduction, and potential income [...] Read more.
This study evaluates the performance of a commercial silicon-based photovoltaic (PV) module under varying environmental conditions, including solar irradiance, module and ambient temperatures, humidity, and wind speed. Key performance indicators such as daily and lifetime energy output, CO2 reduction, and potential income were analyzed. Machine learning techniques, including Linear Regression (LR), Artificial Neural Networks (ANN), Random Forest (RF), and XGBoost, were employed to predict photovoltaic (PV) efficiency under varying environmental conditions. The results indicate that solar irradiance is the primary driver of energy production, while elevated temperatures and high humidity reduce efficiency, and wind speed provides minor cooling benefits. Among the models, XGBoost achieved the highest predictive accuracy (Test R2 = 0.9967), followed by RF and ANN, whereas LR underperformed due to a limited ability to capture nonlinear interactions. These findings highlight the critical influence of environmental and electrical factors on PV performance and demonstrate the effectiveness of advanced machine learning techniques, particularly XGBoost, in optimizing energy output and supporting sustainable energy planning. Full article
(This article belongs to the Topic Artificial Intelligence and Deep Learning for Energy Systems and Power Systems)
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