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Artificial Intelligence in Photovoltaic Systems: Advanced Modeling, Optimization, Forecasting, and Fault Diagnosis

A Special Issue of Energies (ISSN 1996-1073) belonging to the section "A2: Solar Energy and Photovoltaic Systems".

Deadline for manuscript submissions: closed (24 April 2026) | Viewed by 8262

Editors

Prof. Dr. Gang Xiong

E-Mail Website
Guest Editor
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Interests: intelligent control and management; artificial intelligence/hybrid intelligence; intelligent energy; IoT/IoV; cloud computing and big data; intelligent transportation system; smart city; intelligent logistics
Special Issues, Collections and Topics in MDPI journals
Dr. Ehtisham Lodhi

E-Mail Website
Guest Editor
ZJU-UIUC Institute, Zhejiang University, Haining 314400, China
Interests: artificial intelligence; machine learning; deep learning; renewable energy; photovoltaics; mppt techniques; fault detection

Special Issue Information

Dear Colleagues,

The global shift toward clean and sustainable energy has underscored the strategic role of photovoltaic (PV) systems in modern power infrastructure. As PV technologies expand across utility-scale, commercial, and residential sectors, ensuring efficient and reliable operation under dynamic real-world conditions has become increasingly critical. These systems face various challenges, including environmental variability, performance degradation, power generation uncertainty, and operational faults. Addressing these complexities necessitates the development of intelligent, adaptive, and autonomous solutions. In this context, Artificial Intelligence (AI) has emerged as a transformative enabler, driving innovation across the PV system lifecycle—from predictive modeling and control to fault diagnosis and system optimization. This Special Issue aims to showcase cutting-edge research integrating AI methodologies, such as machine learning, deep learning, reinforcement learning, fuzzy logic, and expert systems, in designing, monitoring, managing, and enhancing PV systems.

By leveraging data-driven techniques, researchers are developing intelligent control architectures, accurate forecasting models, and robust diagnostic tools that collectively improve solar power systems' efficiency, resilience, and lifespan. This Special Issue focuses on advanced AI-driven strategies that transcend traditional engineering practices, contributing to more effective energy management, reduced operational costs, and enhanced system performance, particularly under variable environmental conditions. Further, the Issue will explore the integration of AI in hybrid energy systems, IoT-based PV monitoring, real-time optimization, and seamless interaction with smart grids. It seeks to bridge theoretical advances with practical applications, fostering interdisciplinary collaboration between academia and industry.

We welcome original research articles, comprehensive reviews, and case studies presenting novel theoretical insights, algorithmic developments, experimental validations, and real-world implementations. Contributions from power electronics, control systems, computer science, and energy engineering are particularly encouraged to support the advancement of intelligent and autonomous PV technologies in the pursuit of a sustainable energy future.

Topics of interest include, but are not limited to:

  • AI-based modeling and performance prediction;
  • Predicting photovoltaic (PV) performance;
  • AI-driven system optimization and control;
  • AI-enabled solar forecasting;
  • Intelligent fault detection and diagnosis;
  • Real-time monitoring and predictive maintenance;
  • IoT and smart grid integration for PV systems;
  • Hybrid and multi-agent AI approaches;
  • AI for energy storage and microgrid management;
  • AI applications in the sustainable energy transition.

We look forward to considering your submissions.

Prof. Dr. Gang Xiong
Dr. Ehtisham Lodhi
Guest Editors

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. Energies is an international peer-reviewed open access semimonthly 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 2600 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

  • artificial intelligence (AI)
  • photovoltaic systems (PV)
  • solar energy
  • performance prediction
  • fault detection
  • predictive maintenance
  • maximum power point tracking (MPPT)
  • PV system optimization
  • solar forecasting
  • energy storage
  • microgrid management
  • smart grid integration
  • IoT in renewable energy
  • hybrid AI models
  • multi-agent systems
  • data-driven control
  • sustainable energy transition

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Published Papers (3 papers)

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Research

30 pages, 8144 KB  
Article
Benchmarking RF, KNN, MLP, and CNN for FFT-Based PV Arc Fault Detection: Scaling Choice, Temporal Cross-Validation, and Latency Trade-Offs Toward Edge Deployment
by Michel Braulio de Oliveira, Filipe Ramos, José Cesar de Souza Almeida Neto, Fábio Jesus Moreira Almeida and Bruno Luis Soares Lima
Energies 2026, 19(16), 3787; https://doi.org/10.3390/en19163787 - 12 Aug 2026
Viewed by 239
Abstract
Ensuring the safety and reliability of photovoltaic (PV) installations requires accurate electrical arc fault detection. This work presents a computational arc fault detection framework that combines fixed-length windowing, Fast Fourier Transform (FFT)-based features, and supervised machine learning classifiers. Data were acquired using an [...] Read more.
Ensuring the safety and reliability of photovoltaic (PV) installations requires accurate electrical arc fault detection. This work presents a computational arc fault detection framework that combines fixed-length windowing, Fast Fourier Transform (FFT)-based features, and supervised machine learning classifiers. Data were acquired using an Arc Fault Circuit Interrupter (AFCI) test bench developed based on IEC 63027. Current and voltage signals were partitioned into 200-sample windows, DC-offset corrected, and Hann-windowed signals. Each window generated 204 statistical and spectral attributes used to train and evaluate Random Forest (RF), K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), and Convolutional Neural Network (CNN) models. Hyperparameters were tuned by grid search with TimeSeriesSplit cross-validation, comparing min–max normalization and Z–Score standardization. On a 15% hold-out test set, CNN with Z–Score achieved F1 = 0.9982 and recall = 0.9975, followed by MLP (F1 = 0.9957) and RF (F1 = 0.9821). Amortized per-window inference latencies were ≈0.0035 ms for RF, ≈0.0016 ms for MLP with Z–Score, and ≈0.032 ms for CNN. These classifier-stage timings indicate computational compatibility with edge-oriented implementation but do not constitute an end-to-end IEC 63027 AFCI compliance assessment. The framework targets integration into PV inverters at Mackenzie Presbyterian University’s solar plant. Full article
(This article belongs to the Special Issue Artificial Intelligence in Photovoltaic Systems: Advanced Modeling, Optimization, Forecasting, and Fault Diagnosis)
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20 pages, 3757 KB  
Article
Short-Term Photovoltaic Power Forecasting Using a Hybrid RF-ICEEMDAN-SE-RWCE-GRU Model
by Chuang Li, Xiaohuang Huang, Mang Su, Huanhuan Duan, Weile Cao and Guomin Cui
Energies 2026, 19(6), 1386; https://doi.org/10.3390/en19061386 - 10 Mar 2026
Cited by 1 | Viewed by 767
Abstract
To enhance the accuracy of short-term photovoltaic (PV) power forecasting, this study proposes a novel hybrid model that integrates Random Forest (RF), Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), Sample Entropy (SE), the Random Walk with Compulsory Evolution (RWCE) algorithm, [...] Read more.
To enhance the accuracy of short-term photovoltaic (PV) power forecasting, this study proposes a novel hybrid model that integrates Random Forest (RF), Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), Sample Entropy (SE), the Random Walk with Compulsory Evolution (RWCE) algorithm, and the Gated Recurrent Unit (GRU) network. Initially, RF is applied to select relevant meteorological features, minimizing redundancy and improving both training efficiency and predictive robustness under complex operating conditions. ICEEMDAN is then employed to decompose the PV power series into multiple quasi-stationary components, mitigating the adverse effects of non-stationarity on forecasting accuracy. Following this, SE is applied to quantify the complexity of each component and reconstruct the decomposed signals into high-, mid-, and low-frequency bands, simplifying the inputs to the forecasting model. To further improve performance, the RWCE algorithm optimizes GRU network hyperparameters through global exploration, individual evolution, and enforced evolution strategies. The optimized GRU network then predicts each reconstructed component, and the component-wise forecasts are aggregated to yield the final PV power output. Simulation results from several representative months indicate that the proposed approach reduces RMSE by an average of 9.02% compared to comparison model and by 43.41% relative to the baseline model, demonstrating its superior forecasting capability. Additionally, the model demonstrated scalability across varying climate conditions, confirming its applicability in real-world scenarios. Full article
(This article belongs to the Special Issue Artificial Intelligence in Photovoltaic Systems: Advanced Modeling, Optimization, Forecasting, and Fault Diagnosis)
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17 pages, 2518 KB  
Article
A Methodological Framework for Studying the Tilt Angle of Solar Photovoltaic Panels
by Vitālijs Osadčuks, Dainis Berjoza, Jānis Lāceklis-Bertmanis and Ināra Jurgena
Energies 2025, 18(13), 3487; https://doi.org/10.3390/en18133487 - 2 Jul 2025
Cited by 2 | Viewed by 6219
Abstract
With the development of alternative energy technologies, energy production from renewable sources is gaining wide application. One of the types of renewable energy sources is solar power. In the past 5 years, solar cells have become very popular for both private electricity microgeneration [...] Read more.
With the development of alternative energy technologies, energy production from renewable sources is gaining wide application. One of the types of renewable energy sources is solar power. In the past 5 years, solar cells have become very popular for both private electricity microgeneration and large power plants. There are two main options for installing solar photovoltaic panels: on the roof of a house or the ground; on specially made frames. When installing solar cells on the roof, it is not always possible to choose a tilt angle that is appropriate for all seasons, since the angle is mainly adjusted to the plane of the roof. When installing solar cells on the ground, it is usually possible to choose both the orientation relative to the cardinal points and the tilt angle relative to the ground. There are various theories about the best tilt angle of solar cells for producing the most amount of energy during the year. Therefore, the aim of the present research study is to develop an original research methodology for determining an optimal tilt angle for solar cells. The research study examined six different tilt angles of solar cells, 0°, 30°, 35° 40° 45° and 50°, orienting the cells towards the south. The research study used 18 identical monocrystalline solar panels with a power of 20 W. Three solar panels were set at each angle. This way, the experiment had three replications at each angle of the solar cells. The measurements were recorded by a GWL840 data logger with an interval of 10 s. The experiment was conducted by placing all solar cell modules on the roof of the building at Lat. 56.66181° and Long. 23.75238°. During the experimental period, the highest efficiency was found for the solar panels set at 50° and 40°, reaching the total solar irradiation of 266.61 Wm−2 and 266.27 Wm−2, respectively. Full article
(This article belongs to the Special Issue Artificial Intelligence in Photovoltaic Systems: Advanced Modeling, Optimization, Forecasting, and Fault Diagnosis)
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