Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.7
5.2
Journals
Technologies
Special Issues
Artificial Intelligence for Energy Integration and Efficiency in Photovoltaic and...
share announcement

Artificial Intelligence for Energy Integration and Efficiency in Photovoltaic and Thermal Solar Systems

A Special Issue of Technologies (ISSN 2227-7080).

Deadline for manuscript submissions: 31 March 2027 | Viewed by 12847

Editors

Prof. Dr. Juvenal Rodriguez-Resendiz

E-Mail Website
Guest Editor
Facultad de Ingeniería, Universidad Autónoma de Querétaro, Querétaro, Mexico
Interests: machine learning; intelligent systems; signal processing; industrial automation; optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Luis Angel Iturralde Carrera

E-Mail Website
Guest Editor
Facultad de Ingeniería, Universidad Autónoma de Querétaro, Querétaro, Mexico
Interests: renewable energy systems; solar energy engineering; photovoltaic systems; thermofluid systems; energy efficiency; energy management systems; artificial intelligence in engineering; machine learning; computational modeling; optimization algorithms; hybrid energy systems; CFD applications; thermal energy systems; sustainable engineering; digital twins; and intelligent energy management
Special Issues, Collections and Topics in MDPI journals
Dr. Marcos Aviles

E-Mail Website
Guest Editor
Facultad de Ingeniería, Universidad Autónoma de Querétaro, Santiago de Querétaro 76010, Mexico
Interests: EMG; EEG; machine learning; metaheuristics; signal and image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Perla Sevilla-Camacho

E-Mail Website
Guest Editor
1. Cuerpo Académico de Energía y Sustentabilidad, Universidad Politécnica de Chiapas, Carretera Tuxtla Gutiérrez—Portillo Zaragoza Km 21+500, Col. Las Brisas, Suchiapa C.P. 29150, Mexico
2. Programa Académico de Ingeniería Mecatrónica, Universidad Politécnica de Chiapas, Carretera Tuxtla Gutiérrez—Portillo Zaragoza Km 21+500, Col. Las Brisas, Suchiapa C.P. 29150, Mexico
Interests: wind power

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to this Special Issue, focused on advances in artificial intelligence (AI) applied to the integration and energy efficiency of photovoltaic and thermal solar systems.

In the face of the growing global energy demand and the urgent need for sustainable solutions, the application of AI techniques in solar energy systems has emerged as a powerful tool to optimize performance, enhance energy management, and support smart decision-making. From machine learning algorithms that forecast solar irradiance and energy production to intelligent controllers that regulate thermal storage and photovoltaic conversion, AI is transforming the way we design, operate, and integrate solar technologies.

This Special Issue seeks to collate high-quality contributions that demonstrate the use of AI in enhancing the efficiency, reliability, and scalability of solar systems. Topics of interest include, but are not limited to, the following:

  • Intelligent forecasting of solar irradiance and energy demand;
  • AI-based optimization of PV and thermal system performance;
  • Smart control systems for hybrid solar configurations;
  • Predictive maintenance using AI for solar installations;
  • Energy integration in smart grids with AI-enhanced coordination;
  • Deep learning approaches for fault detection and diagnostics;
  • Reinforcement learning for adaptive energy management;
  • Case studies in real-world applications and industrial implementations.

The aim of this Special Issue is to foster interdisciplinary research and share innovative solutions that bridge the gap between AI and sustainable energy engineering. We welcome the submission of original research articles, reviews, and case studies that explore novel methodologies, implementations, and theoretical insights.

We look forward to receiving your valuable contributions to this timely and impactful collection.

Prof. Dr. Juvenal Rodriguez-Resendiz
Dr. Luis Angel Iturralde Carrera
Dr. Marcos Aviles
Dr. Perla Sevilla-Camacho
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. Technologies 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

  • artificial intelligence in energy systems
  • machine learning for solar energy
  • optimization algorithms for photovoltaic and thermal systems
  • smart solar energy management
  • predictive control in renewable energy
  • neural networks for energy forecasting
  • intelligent system integration
  • data-driven energy optimization
  • AI in embedded solar systems
  • energy efficiency through computational intelligence

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review, Other

43 pages, 5138 KB  
Article
Air-to-Air Flight: ANFIS-Assisted Multi-Pack LiPo Battery Charging System for Continuous Flying Missions of UAVs
by Essam Ali, Mohamed Abdelrahem, José Rodríguez, Abdelfatah M. Mohamed and Alaaeldin M. Abdelshafy
Technologies 2026, 14(6), 379; https://doi.org/10.3390/technologies14060379 - 22 Jun 2026
Cited by 1 | Viewed by 578
Abstract
Continouous unmanned aerial vehicle (UAV) missions are fundamentally limited by Lithium-Polymer (LiPo) battery endurance under intermittent and power-constrained renewable energy conditions. This paper proposes an integrated energy management and charging framework for a photovoltaic (PV)-powered mobile station equipped with a hybrid energy storage [...] Read more.
Continouous unmanned aerial vehicle (UAV) missions are fundamentally limited by Lithium-Polymer (LiPo) battery endurance under intermittent and power-constrained renewable energy conditions. This paper proposes an integrated energy management and charging framework for a photovoltaic (PV)-powered mobile station equipped with a hybrid energy storage system (HESS) and an automated battery replacement (ABR) mechanism. A lexicographic priority-based allocator sequentially serves ABR actuation, multi-slot LiPo charging, and Brushless DC (BLDC) propulsion, while the HESS compensates for PV intermittency. At the charging level, a constraint-aware constant current–constant voltage (CC–CV) strategy is enhanced by an adaptive neuro-fuzzy inference system (ANFIS) trained on optimization-derived labels using battery temperature and its rate of change, thus enabling anticipatory thermal current derating with smooth, discontinuity-free control action. Anti-windup proportional–integral (PI) regulation and bumpless mode transfer ensure stable CC-to-CV transitions. An event-triggered emergency mode accelerates battery readiness via a max-first selection policy. Comparative simulations against a PSO/DE-optimized PID benchmark over a full diurnal PV cycle demonstrate that the ANFIS controller reduces the CC-mode current tracking root-mean-square error (RMSE) by up to 96.9%, delivers higher charge throughput, and lowers battery degradation proxies, including SOC-weighted thermal dose and equivalent full cycles (EFC). The proposed framework reliably sustains continuous charge–swap–recharge logistics under fluctuating renewable generation. Full article
(This article belongs to the Special Issue Artificial Intelligence for Energy Integration and Efficiency in Photovoltaic and Thermal Solar Systems)
►▼ Show Figures

Figure 1

33 pages, 5543 KB  
Article
Structural Optimization of a Hybrid Fuzzy–Incremental Conductance MPPT Controller for Photovoltaic Systems with Battery Storage
by Ezequiel Rincon-Canalizo, David Gutiérrez-Rosales, Daniel Aguilar-Torres, Omar Jiménez-Ramírez and Rubén Vázquez-Medina
Technologies 2026, 14(6), 374; https://doi.org/10.3390/technologies14060374 - 18 Jun 2026
Cited by 1 | Viewed by 1170
Abstract
This study presents a hybrid controller that integrates fuzzy logic control and the Incremental Conductance method. This controller optimizes maximum power point tracking in a 330 W photovoltaic system by designing a DC-DC converter. The study evaluates how the number and distribution of [...] Read more.
This study presents a hybrid controller that integrates fuzzy logic control and the Incremental Conductance method. This controller optimizes maximum power point tracking in a 330 W photovoltaic system by designing a DC-DC converter. The study evaluates how the number and distribution of membership functions, specifically three-, five-, and seven-function configurations, affect system performance using the Integral Square Error (ISE) and Integral Absolute Error (IAE) indices. The empirical results demonstrate that the seven-function architecture yields optimal performance, minimizing ISE and IAE to 0.1155 and 7.365×10−4, respectively. Furthermore, this optimal configuration attains an energy efficiency of 99.7%, notably outperforming the baseline three-function configuration, which exhibited a worst-case efficiency of 98.9 %. To assess robustness against dynamic environmental variations, this study subjects the optimal configuration to fluctuating irradiance and temperature profiles. Additionally, an analysis of computational resource consumption reveals that the proposed hybrid controller incurs a lower computational load for rule evaluation than three controllers reported in the recent literature. These findings demonstrate the system’s structural efficiency and superior optimization capability, achieving maximized photovoltaic energy harvesting at a low computational cost. Full article
(This article belongs to the Special Issue Artificial Intelligence for Energy Integration and Efficiency in Photovoltaic and Thermal Solar Systems)
►▼ Show Figures

Figure 1

23 pages, 1837 KB  
Article
Use of Machine Learning for Solar Power Generation Prediction in the Field of Alternative Renewable Energy Sources
by Juan D. Parra-Quintero, Daniel Ovalle-Cerquera, Edwin Chica and Ainhoa Rubio-Clemente
Technologies 2026, 14(4), 206; https://doi.org/10.3390/technologies14040206 - 31 Mar 2026
Cited by 1 | Viewed by 1708
Abstract
This study focused on the application of supervised learning in the field of renewable energy, specifically for predicting daily solar irradiance in Neiva, department of Huila, Colombia. To this end, decision tree and artificial neural network (DT and ANN, respectively) models were trained [...] Read more.
This study focused on the application of supervised learning in the field of renewable energy, specifically for predicting daily solar irradiance in Neiva, department of Huila, Colombia. To this end, decision tree and artificial neural network (DT and ANN, respectively) models were trained and tested using the online tool Google Colab. The main objective was based on the need to optimize energy planning processes at local and regional levels, motivated by the increase in demand for the integration of non-conventional energy sources and the spatial–temporal variability in solar resources in the country. A dataset consisting of 366 daily records for the year 2024 was obtained from the NASA POWER database at the geographic coordinates (2.930079, −75.255650) and used for training and evaluating the proposed models. Statistical and cleaning techniques were used, including the treatment of outliers using the moving-window median for the latter. Metrics, such as mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2), were used to evaluate the models. Data inclusion and exclusion criteria were applied to ensure the quality and validity of the observations. Model performance was evaluated using a randomized Hold-Out validation strategy (90% training and 10% testing), which was repeated across multiple iterations. The performance metrics reported corresponded to the 10th iteration of the validation process after outlier treatment. Under this configuration, the DT model achieved a higher predictive performance (R2 = 0.8882) compared with the ANN model (R2 = 0.7679), demonstrating its effectiveness as a reliable approach for estimating daily solar irradiance under the studied conditions. This result was also confirmed by the decreased MAE and RMSE for the DT model, which indicated that this model performed better in predicting the real values than the ANN model. Finally, the added value of the study is to consolidate national evidence and open access tools to facilitate the development of sustainable energy policies in intermediate cities such as Neiva. Full article
(This article belongs to the Special Issue Artificial Intelligence for Energy Integration and Efficiency in Photovoltaic and Thermal Solar Systems)
►▼ Show Figures

Figure 1

Review

Jump to: Research, Other

32 pages, 1924 KB  
Review
A Review of Mamdani, Takagi–Sugeno, and Type-2 Fuzzy Controllers for MPPT and Power Management in Photovoltaic Systems
by Rodrigo Vidal-Martínez, José R. García-Martínez, Rafael Rojas-Galván, José M. Álvarez-Alvarado, Mario Gozález-Lee and Juvenal Rodríguez-Reséndiz
Technologies 2025, 13(9), 422; https://doi.org/10.3390/technologies13090422 - 20 Sep 2025
Cited by 15 | Viewed by 5247
Abstract
This review presents a synthesis of fuzzy logic-based (FL) controllers applied to photovoltaic (PV) systems over the last decade, with a specific focus on maximum power point tracking (MPPT) and power management. These subsystems are critical for improving the efficiency of PV energy [...] Read more.
This review presents a synthesis of fuzzy logic-based (FL) controllers applied to photovoltaic (PV) systems over the last decade, with a specific focus on maximum power point tracking (MPPT) and power management. These subsystems are critical for improving the efficiency of PV energy conversion, as they directly address the nonlinear, time-varying, and uncertain behavior of solar generation under dynamic environmental conditions. FL-based control has proven to be a powerful and versatile tool for enhancing MPPT accuracy, inverter performance, and hybrid energy management strategies. The analysis concentrates on three main categories, namely, Mamdani, Takagi–Sugeno (T-S), and Type-2, highlighting their architectures, operational characteristics, and application domains. Mamdani controllers remain the most widely adopted due to their simplicity, interpretability, and effectiveness in scenarios with moderate response time requirements. T-S controllers excel in real-time high-frequency operations by eliminating the defuzzification stage and approximating system nonlinearities through local linear models, achieving rapid convergence to the maximum power point (MPP) and improved power quality in grid-connected PV systems. Type-2 fuzzy controllers represent the most advanced evolution, incorporating footprints of uncertainty (FOU) to handle high variability, sensor noise, and environmental disturbances, thereby strengthening MPPT accuracy under challenging conditions. This review also examines the integration of metaheuristic algorithms for automated tuning of membership functions and hybrid architectures that combine fuzzy control with artificial intelligence (AI) techniques. A bibliometric perspective reveals a growing research interest in T-S and Type-2 approaches. Quantitatively, Mamdani controllers account for 54.20% of publications, T-S controllers for 26.72%, and Type-2 fuzzy controllers for 19.08%, reflecting the balance between interpretability, computational performance, and robustness to uncertainty in PV-based MPPT and power management applications. Full article
(This article belongs to the Special Issue Artificial Intelligence for Energy Integration and Efficiency in Photovoltaic and Thermal Solar Systems)
►▼ Show Figures

Graphical abstract

Other

Jump to: Research, Review

32 pages, 4849 KB  
Systematic Review
Artificial Intelligence in Solar-Assisted Greenhouse Systems: A Technical, Systematic and Bibliometric Review of Energy Integration and Efficiency Advances
by Edwin Villagran, John Javier Espitia, Fabián Andrés Velázquez, Andres Sarmiento, Diego Alejandro Salinas Velandia and Jader Rodriguez
Technologies 2025, 13(12), 574; https://doi.org/10.3390/technologies13120574 - 6 Dec 2025
Cited by 11 | Viewed by 2744
Abstract
Protected agriculture increasingly requires solutions that reduce energy consumption and environmental impacts while maintaining stable microclimatic conditions. The integration of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) with solar technologies has emerged as a pathway toward autonomous and energy-efficient greenhouses [...] Read more.
Protected agriculture increasingly requires solutions that reduce energy consumption and environmental impacts while maintaining stable microclimatic conditions. The integration of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) with solar technologies has emerged as a pathway toward autonomous and energy-efficient greenhouses and solar dryers. This study analyzes the scientific and technological evolution of this convergence using a mixed review approach bibliometric and systematic, following PRISMA 2020 guidelines. From Scopus records (2012–2025), 115 documents were screened and 79 met the inclusion criteria. Bibliometric results reveal accelerated growth since 2019, led by Engineering, Computer Science, and Energy, with China, India, Saudi Arabia, and the United Kingdom as dominant contributors. Thematic analysis identifies four major research fronts: (i) thermal modeling and energy efficiency, (ii) predictive control and microclimate automation, (iii) integration of photovoltaic–thermal (PV/T) systems and phase change materials (PCMs), and (iv) sustainability and agrivoltaics. Systematic evidence shows that AI, ML, and DL based models improve solar forecasting, microclimate regulation, and energy optimization; model predictive control (MPC), deep reinforcement learning (DRL), and energy management systems (EMS) enhance operational efficiency; and PV/T–PCM hybrids strengthen heat recovery and storage. Remaining gaps include long-term validation, metric standardization, and cross-context comparability. Overall, the field is advancing toward near-zero-energy greenhouses powered by Internet of Things (IoT), AI, and solar energy, enabling resilient, efficient, and decarbonized agro-energy systems. Full article
(This article belongs to the Special Issue Artificial Intelligence for Energy Integration and Efficiency in Photovoltaic and Thermal Solar Systems)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop