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Algorithms
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Algorithmic Approaches and Artificial Intelligence in Photovoltaic Systems
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Algorithmic Approaches and Artificial Intelligence in Photovoltaic Systems

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 978

Editor

Prof. Dr. Simon Foo

E-Mail Website
Guest Editor
Department of Electrical & Computer Engineering, Florida State University, Tallahassee, FL 32310-6046, USA
Interests: photovoltaics; multi-junction iii-v compound solar cells; organic/polymer solar cells; quantum dot solar cells; perovskite solar cells; machine learning; deep learning; artificial intelligence

Special Issue Information

Dear Colleagues,

The rapid growth of artificial intelligence (AI) and algorithm optimization has changed the field of photovoltaic (PV) energy systems. As the world pushes for sustainable and carbon-neutral energy, AI-driven algorithms are becoming essential tools for improving the efficiency, reliability, and intelligence of PV systems. This applies to everything from material design and power forecasting to fault detection, control, and grid integration.

Recent advancements in deep learning, reinforcement learning, evolutionary computing, and optimization algorithms have revealed new ways to harvest and manage solar energy. These methods enable breakthroughs in areas like real-time energy forecasting, adaptive maximum power point tracking (MPPT), predictive maintenance, degradation modeling, and smart control of hybrid renewable systems. In addition, AI-based analytical frameworks are increasingly used for performance prediction, fault diagnosis, and autonomous decision making in large solar farms and microgrids.

This Special Issue offers a space for researchers and practitioners to share new methods, frameworks, and applications of AI and algorithm strategies in photovoltaic systems. We especially welcome contributions that focus on innovative algorithms, hybrid modeling techniques, data-driven system optimization, and real-world applications.

The topics include, but are not limited to, the following:

  • Machine learning and deep learning for PV performance prediction;
  • Reinforcement learning and control strategies for MPPT and grid integration;
  • Evolutionary and swarm intelligence optimization for PV system design;
  • AI-based energy forecasting and load management;
  • Fault detection, diagnosis, and predictive maintenance using AI;
  • AI-assisted material discovery and photovoltaic device optimization;
  • Hybrid renewable energy systems incorporating PV and AI control;
  • Edge intelligence and IoT for real-time solar energy management;
  • Explainable and interpretable AI models for PV applications.

Prof. Dr. Simon Foo
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • photovoltaic systems
  • machine learning
  • deep learning
  • swarm intelligence optimization
  • fault detection
  • explainable and interpretable AI

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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24 pages, 2643 KB  
Article
A Cross-Variable Time-Series Transformer Architecture Integrating Physical Features for Photovoltaic Energy Forecasting
by Chen Xie, Mingju Chen, Yuyan Wang, Yangming Luo, Xueyang Duan and Zhihao Lin
Algorithms 2026, 19(8), 646; https://doi.org/10.3390/a19080646 - 5 Aug 2026
Viewed by 182
Abstract
Accurate photovoltaic (PV) energy forecasting is vital for grid stability and the global low-carbon transition. However, existing data-driven and channel-independent PV energy forecasting models struggle to capture nonlinear meteorological couplings, heterogeneous physical scales across stations, and high-frequency non-stationary fluctuations. To address these limitations, [...] Read more.
Accurate photovoltaic (PV) energy forecasting is vital for grid stability and the global low-carbon transition. However, existing data-driven and channel-independent PV energy forecasting models struggle to capture nonlinear meteorological couplings, heterogeneous physical scales across stations, and high-frequency non-stationary fluctuations. To address these limitations, this study proposes a Physics-Guided Cross-Variable Temporal Transformer architecture. Building upon a channel-independent foundation, we introduce a Cross-Variable Attention mechanism to explicitly reconstruct nonlinear photothermal couplings via dynamic attention weights. To resolve multi-station physical scale discrepancies, a Physical Feature-wise Linear Modulation network utilizes installed capacity as a static prior for adaptive cross-station scale alignment. During optimization, a Time Dynamics-Aware Perceiving Loss jointly penalizes absolute errors and first-order time differences, constraining the network’s tracking ability for transient ramping. Experiments demonstrate that the proposed architecture overcomes traditional channel-isolation limitations. The model achieves a 21.6% reduction in MSE compared to PatchTST, a 44.0% reduction compared to Autoformer, and a 2.7% improvement in R2 over Informer. This provides an accurate, generalizable, and physically interpretable solution for collaborative multi-station distributed PV energy forecasting. Full article
(This article belongs to the Special Issue Algorithmic Approaches and Artificial Intelligence in Photovoltaic Systems)
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22 pages, 4109 KB  
Article
An Algorithmic Framework for Plant-Level AC Power Estimation in a Bifacial Horizontal Single-Axis Tracking PV System Using Explainable and Ensemble Machine Learning
by Luis Fernando Bustos-Marquez and Steven Hegedus
Algorithms 2026, 19(6), 496; https://doi.org/10.3390/a19060496 - 22 Jun 2026
Viewed by 367
Abstract
Accurate plant-level photovoltaic (PV) power estimation is important for performance monitoring, model benchmarking, and grid-integration studies. In bifacial horizontal single-axis tracking (HSAT) systems, this task is complicated by the coupled effects of front-side irradiance, rear-side irradiance, tracker position, and module temperature. This study [...] Read more.
Accurate plant-level photovoltaic (PV) power estimation is important for performance monitoring, model benchmarking, and grid-integration studies. In bifacial horizontal single-axis tracking (HSAT) systems, this task is complicated by the coupled effects of front-side irradiance, rear-side irradiance, tracker position, and module temperature. This study proposes an algorithmic framework for same-time-step AC power estimation in a bifacial HSAT PV plant using field measurements of irradiance, tracker angle, module temperature, and inverter active power. The framework is not intended as an operational forecasting model because future irradiance and weather conditions are not predicted; instead, it evaluates how compact physics-based structure, interpretable nonlinear learning, and ensemble learning estimate measured AC power under nominal operating conditions. An empirical rear-to-front irradiance relationship was derived using solar-elevation bins and incorporated into a compact physics-based benchmark. This benchmark was compared with an additive Explainable Boosting Machine (EBM) and a Random Forest (RF) on a common test subset of 3916 observations. The physics-based model achieved an RMSE of 19.6 kW, an R2 of 0.72, and an NRMSE of 0.38. The EBM improved these values to 17.09 kW, 0.786, and 0.334, respectively, while the RF achieved 15.96 kW, 0.814, and 0.312. Chronological validation showed weaker and more variable performance than randomized validation, indicating that temporal generalization remains challenging. Overall, the results support the use of interpretable PV-domain-guided learning as a transparent intermediate approach between compact physics-based modeling and more flexible ensemble regression. Full article
(This article belongs to the Special Issue Algorithmic Approaches and Artificial Intelligence in Photovoltaic Systems)
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