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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A2: Solar Energy and Photovoltaic Systems".

Deadline for manuscript submissions: closed (20 March 2023) | Viewed by 34616

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Special Issue Editors

Dr. Fouzi Harrou

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Guest Editor

1. Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
2. King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
Interests: fault detection and diagnosis; deep learning and machine learning; wind and solar power forecasting; renewable energy systems
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Dr. Ying Sun

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Assistant Guest Editor

Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
Interests: environmental statistics, in particular in the areas of spatiotemporal statistics; functional data analysis; visualization; computational statistics, with an exceptionally broad array of applications
Special Issues, Collections and Topics in MDPI journals

Dr. Bilal Taghezouit

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Assistant Guest Editor

Renewable Energy Development Center (CDER), Algiers, Algeria
Interests: measurement; modeling; monitoring; performance analysis; and fault detection of PV systems

Dr. Abdelkader Dairi

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Assistant Guest Editor

Computer Science Department Signal, Image and Speech Laboratory (SIMPA) Laboratory, Oran University of Science and Technology-Mohamed Boudiaf, Oran, Algeria
Interests: programming languages; artificial intelligence; computer vision; machine learning; deep learning and applications to renewable energy systems

Special Issue Information

Dear Colleague,

The main difficulty in solar energy production is the volatility intermittent of photovoltaic system power generation due mainly to weather conditions. Essentially, the variation of the temperature and irradiance can profoundly impact the quality of electric power production. As solar irradiance is highly related to solar power harvesting, its prediction can be a good indicator of power production. For large-scale solar farms, the power imbalance of the photovoltaic system may cause a significant loss in their economic profit. Thus, accurate solar irradiance prediction with appropriate modeling of PV systems is becoming vital to reduce the impact of uncertainty and energy costs and enable the suitable integration of photovoltaic systems in a smart grid. There have been many studies for models and algorithms to predict solar irradiance based on various meteorological factors that are routinely measured, such as temperature or humidity. Accurate forecast of solar irradiance and proper modeling of PV system behavior have become the backbone of smart grids due to the increasing installation of PV systems.

This Special Issue aims to collect original research or review articles in the areas of artificial intelligence applied to solar irradiance modeling/forecasting and PV system design. Thus, this call seeks submissions on innovative machine learning and deep learning methods for solar irradiance forecasting and PV systems modeling.

Potential topics include but are not limited to:

Solar irradiance modeling and forecasting
Typical meteorological year (TMY) modeling
PV system modeling
Space–time prediction of solar irradiance
Deep learning and machine learning methods
Reinforcement learning

Dr. Fouzi Harrou
Dr. Ying Sun
Dr. Bilal Taghezouit
Dr. Abdelkader Dairi
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 100 words) can be sent to the Editorial Office for announcement on this website.

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-blind 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.

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Published Papers (12 papers)

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Editorial

Jump to: Research

5 pages, 194 KiB

Open AccessEditorial

Artificial Intelligence Techniques for Solar Irradiance and PV Modeling and Forecasting

by Fouzi Harrou, Ying Sun, Bilal Taghezouit and Abdelkader Dairi

Energies 2023, 16(18), 6731; https://doi.org/10.3390/en16186731 - 21 Sep 2023

Cited by 4 | Viewed by 1558

Abstract

Solar Photovoltaic (PV) systems represent key and transformative technology at the forefront of the global shift towards sustainable energy solutions [...] Full article

(This article belongs to the Special Issue Artificial Intelligence Techniques for Solar Irradiance and PV Modeling and Forecasting)

Research

Jump to: Editorial

25 pages, 5990 KiB

Open AccessArticle

Photovoltaic Energy Forecast Using Weather Data through a Hybrid Model of Recurrent and Shallow Neural Networks

by Wilson Castillo-Rojas, Fernando Medina Quispe and César Hernández

Energies 2023, 16(13), 5093; https://doi.org/10.3390/en16135093 - 1 Jul 2023

Cited by 6 | Viewed by 2361

Abstract

In this article, forecast models based on a hybrid architecture that combines recurrent neural networks and shallow neural networks are presented. Two types of models were developed to make predictions. The first type consisted of six models that used records of exported active energy and meteorological variables as inputs. The second type consisted of eight models that used meteorological variables. Different metrics were applied to assess the performance of these models. The best model of each type was selected. Finally, a comparison of the performance between the selected models of both types was presented. The models were validated using real data provided by a solar plant, achieving acceptable levels of accuracy. The selected model of the first type had a root mean square error (RMSE) of 0.19, a mean square error (MSE) of 0.03, a mean absolute error (MAE) of 0.09, a correlation coefficient of 0.96, and a determination coefficient of 0.93. The other selected model of the second type showed lower accuracy in the metrics: RMSE = 0.24, MSE = 0.06, MAE = 0.10, correlation coefficient = 0.95, and determination coefficient = 0.90. Both models demonstrated good performance and acceptable accuracy in forecasting the weekly photovoltaic energy generation of the solar plant. Full article

(This article belongs to the Special Issue Artificial Intelligence Techniques for Solar Irradiance and PV Modeling and Forecasting)

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21 pages, 5636 KiB

Open AccessArticle

IoT-Based Low-Cost Photovoltaic Monitoring for a Greenhouse Farm in an Arid Region

by Amor Hamied, Adel Mellit, Mohamed Benghanem and Sahbi Boubaker

Energies 2023, 16(9), 3860; https://doi.org/10.3390/en16093860 - 30 Apr 2023

Cited by 7 | Viewed by 3178

Abstract

In this paper, a low-cost monitoring system for an off-grid photovoltaic (PV) system, installed at an isolated location (Sahara region, south of Algeria), is designed. The PV system is used to supply a small-scale greenhouse farm. A simple and accurate fault diagnosis algorithm was developed and integrated into a low-cost microcontroller for real time validation. The monitoring system, including the fault diagnosis procedure, was evaluated under specific climate conditions. The Internet of Things (IoT) technique is used to remotely monitor the data, such as PV currents, PV voltages, solar irradiance, and cell temperature. A friendly web page was also developed to visualize the data and check the state of the PV system remotely. The users could be notified about the state of the PV system via phone SMS. Results showed that the system performs better under this climate conditions and that it can supply the considered greenhouse farm. It was also shown that the integrated algorithm is able to detect and identify some examined defects with a good accuracy. The total cost of the designed IoT-based monitoring system is around 73 euros and its average energy consumed per day is around 13.5 Wh. Full article

(This article belongs to the Special Issue Artificial Intelligence Techniques for Solar Irradiance and PV Modeling and Forecasting)

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23 pages, 7197 KiB

Open AccessArticle

Revolutionizing Photovoltaic Systems: An Innovative Approach to Maximum Power Point Tracking Using Enhanced Dandelion Optimizer in Partial Shading Conditions

by Elmamoune Halassa, Lakhdar Mazouz, Abdellatif Seghiour, Aissa Chouder and Santiago Silvestre

Energies 2023, 16(9), 3617; https://doi.org/10.3390/en16093617 - 22 Apr 2023

Cited by 7 | Viewed by 1529

Abstract

Partial shading (PS) is a prevalent phenomenon that often affects photovoltaic (PV) installations, leads to the appearance of numerous peaks in the power-voltage characteristics of PV cells, caused by the uneven distribution of solar irradiance on the PV module surface, known as global and local maximum power point (GMPP and LMPP). In this paper, a new technique for achieving GMPP based on the dandelion optimizer (DO) algorithm is proposed, inspired by the movement of dandelion seeds in the wind. The proposed technique aimed to enhance the efficiency of power generation in PV systems, particularly under PS conditions. However, the DO-based MPPT is compared with other advanced maximum power point tracker (MPPT) algorithms, such as Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Artificial Bee Colony (ABC), Cuckoo Search Algorithm (CSA), and Bat Algorithm (BA). Simulation results establish the superiority and effectiveness of the used MPPT in terms of tracking efficiency, speed, robustness, and simplicity of implementation. Additionally, these results reveal that the DO algorithm exhibits higher performance, with a root mean square error (RMSE) of 1.09 watts, a convergence time of 2.3 milliseconds, and mean absolute error (MAE) of 0.13 watts. Full article

(This article belongs to the Special Issue Artificial Intelligence Techniques for Solar Irradiance and PV Modeling and Forecasting)

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12 pages, 7333 KiB

Open AccessCommunication

Exploring the PV Power Forecasting at Building Façades Using Gradient Boosting Methods

by Jesús Polo, Nuria Martín-Chivelet, Miguel Alonso-Abella, Carlos Sanz-Saiz, José Cuenca and Marina de la Cruz

Energies 2023, 16(3), 1495; https://doi.org/10.3390/en16031495 - 2 Feb 2023

Cited by 7 | Viewed by 1925

Abstract

Solar power forecasting is of high interest in managing any power system based on solar energy. In the case of photovoltaic (PV) systems, and building integrated PV (BIPV) in particular, it may help to better operate the power grid and to manage the power load and storage. Power forecasting directly based on PV time series has some advantages over solar irradiance forecasting first and PV power modeling afterwards. In this paper, the power forecasting for BIPV systems in a vertical façade is studied using machine learning algorithms based on decision trees. The forecasting scheme employs the skforecast library from the Python environment, which facilitates the implementation of different schemes for both deterministic and probabilistic forecasting applications. Firstly, deterministic forecasting of hourly BIPV power was performed with XGBoost and Random Forest algorithms for different cases, showing an improvement in forecasting accuracy when some exogenous variables were used. Secondly, probabilistic forecasting was performed with XGBoost combined with the Bootstrap method. The results of this paper show the capabilities of Random Forest and gradient boosting algorithms, such as XGBoost, to work as regressors in time series forecasting of BIPV power. Mean absolute error in the deterministic forecast, using the most influencing exogenous variables, were around 40% and close below 30% for the south and east array, respectively. Full article

(This article belongs to the Special Issue Artificial Intelligence Techniques for Solar Irradiance and PV Modeling and Forecasting)

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17 pages, 5392 KiB

Open AccessArticle

A Sensorless Intelligent System to Detect Dust on PV Panels for Optimized Cleaning Units

by Faris E. Alfaris

Energies 2023, 16(3), 1287; https://doi.org/10.3390/en16031287 - 25 Jan 2023

Cited by 11 | Viewed by 2468

Abstract

Deployment of photovoltaic (PV) systems has recently been encouraged for large-scale and small-scale businesses in order to meet the global green energy targets. However, one of the most significant hurdles that limits the spread of PV applications is the dust accumulated on the PV panels’ surfaces, especially in desert regions. Numerous studies sought the use of cameras, sensors, power datasets, and other detection elements to detect the dust on PV panels; however, these methods pose more maintenance, accuracy, and economic challenges. Therefore, this paper proposes an intelligent system to detect the dust level on the PV panels to optimally operate the attached dust cleaning units (DCUs). Unlike previous strategies, this study utilizes the expanded knowledge and collected data for solar irradiation and PV-generated power, along with the forecasted ambient temperature. An expert artificial intelligence (AI) computational system, adopted with the MATLAB platform, is utilized for a high level of data prediction and processing. The AI was used in this study in order to estimate the unprovided information, emulate the provided measurements, and accommodate more input/output data. The feasibility of the proposed system is investigated using actual field data during all possible weather conditions. Full article

(This article belongs to the Special Issue Artificial Intelligence Techniques for Solar Irradiance and PV Modeling and Forecasting)

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16 pages, 4857 KiB

Open AccessArticle

Approximating Shading Ratio Using the Total-Sky Imaging System: An Application for Photovoltaic Systems

by Mahmoud Dhimish and Pavlos I. Lazaridis

Energies 2022, 15(21), 8201; https://doi.org/10.3390/en15218201 - 3 Nov 2022

Cited by 3 | Viewed by 1410

Abstract

In recent years, a determined shading ratio of photovoltaic (PV) systems has been broadly reviewed and explained. Observing the shading ratio of PV systems allows us to navigate for PV faults and helps to recognize possible degradation mechanisms. Therefore, this work introduces a novel approximation shading ratio technique using an all-sky imaging system. The proposed solution has the following structure: (i) we determined four all-sky imagers for a region of 25 km², (ii) computed the cloud images using our new proposed model, called color-adjusted (CA), (iii) computed the shading ratio, and (iv) estimated the global horizontal irradiance (GHI) and consequently, obtained the predicted output power of the PV system. The estimation of the GHI was empirically compared with captured data from two different weather stations; we found that the average accuracy of the proposed technique was within a maximum ±12.7% error rate. In addition, the PV output power approximation accuracy was as high as 97.5% when the shading was zero and reduced to the lowest value of 83% when overcasting conditions affected the examined PV system. Full article

(This article belongs to the Special Issue Artificial Intelligence Techniques for Solar Irradiance and PV Modeling and Forecasting)

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28 pages, 3530 KiB

Open AccessArticle

Ensemble Learning Techniques-Based Monitoring Charts for Fault Detection in Photovoltaic Systems

by Fouzi Harrou, Bilal Taghezouit, Sofiane Khadraoui, Abdelkader Dairi, Ying Sun and Amar Hadj Arab

Energies 2022, 15(18), 6716; https://doi.org/10.3390/en15186716 - 14 Sep 2022

Cited by 16 | Viewed by 2107

Abstract

Over the past few years, there has been a significant increase in the interest in and adoption of solar energy all over the world. However, despite ongoing efforts to protect photovoltaic (PV) plants, they are continuously exposed to numerous anomalies. If not detected accurately and in a timely manner, anomalies in PV plants may degrade the desired performance and result in severe consequences. Hence, developing effective and flexible methods capable of early detection of anomalies in PV plants is essential for enhancing their management. This paper proposes flexible data-driven techniques to accurately detect anomalies in the DC side of the PV plants. Essentially, this approach amalgamates the desirable characteristics of ensemble learning approaches (i.e., the boosting (BS) and bagging (BG)) and the sensitivity of the Double Exponentially Weighted Moving Average (DEWMA) chart. Here, we employ ensemble learning techniques to exploit their capability to enhance the modeling accuracy and the sensitivity of the DEWMA monitoring chart to uncover potential anomalies. In the ensemble models, the values of parameters are selected with the assistance of the Bayesian optimization algorithm. Here, BS and BG are adopted to obtain residuals, which are then monitored by the DEWMA chart. Kernel density estimation is utilized to define the decision thresholds of the proposed ensemble learning-based charts. The proposed monitoring schemes are illustrated via actual measurements from a 9.54 kW PV plant. Results showed the superior detection performance of the BS and BG-based DEWMA charts with non-parametric threshold in uncovering different types of anomalies, including circuit breaker faults, inverter disconnections, and short-circuit faults. In addition, the performance of the proposed schemes is compared to that of BG and BS-based DEWMA and EWMA charts with parametric thresholds. Full article

(This article belongs to the Special Issue Artificial Intelligence Techniques for Solar Irradiance and PV Modeling and Forecasting)

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17 pages, 14471 KiB

Open AccessArticle

Modeling of Photovoltaic Array Based on Multi-Agent Deep Reinforcement Learning Using Residuals of I–V Characteristics

by Jingwei Zhang, Zenan Yang, Kun Ding, Li Feng, Frank Hamelmann, Xihui Chen, Yongjie Liu and Ling Chen

Energies 2022, 15(18), 6567; https://doi.org/10.3390/en15186567 - 8 Sep 2022

Cited by 4 | Viewed by 1692

Abstract

Currently, the accuracy of modeling a photovoltaic (PV) array for fault diagnosis is still unsatisfactory due to the fact that the modeling accuracy is limited by the accuracy of extracted model parameters. In this paper, the modeling of a PV array based on multi-agent deep reinforcement learning (RL) using the residuals of I–V characteristics is proposed. The environment state based on the high dimensional residuals of I–V characteristics and the corresponding cooperative reward is presented for the RL agents. The actions of each agent considering the damping amplitude are designed. Then, the entire framework of modeling a PV array based on multi-agent deep RL is presented. The feasibility and accuracy of the proposed method are verified by the one-year measured data of a PV array. The experimental results show that the higher modeling accuracy of the next time step is obtained by the extracted model parameters using the proposed method, compared with that using the conventional meta-heuristic algorithms and the analytical method. The daily root mean square error (RMSE) is approximately 0.5015 A on the first day, and converges to 0.1448 A on the last day of training. The proposed multi-agent deep RL framework simplifies the design of states and rewards for extracting model parameters. Full article

(This article belongs to the Special Issue Artificial Intelligence Techniques for Solar Irradiance and PV Modeling and Forecasting)

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22 pages, 5937 KiB

Open AccessArticle

Application of Temporal Fusion Transformer for Day-Ahead PV Power Forecasting

by Miguel López Santos, Xela García-Santiago, Fernando Echevarría Camarero, Gonzalo Blázquez Gil and Pablo Carrasco Ortega

Energies 2022, 15(14), 5232; https://doi.org/10.3390/en15145232 - 19 Jul 2022

Cited by 46 | Viewed by 6583

Abstract

The energy generated by a solar photovoltaic (PV) system depends on uncontrollable factors, including weather conditions and solar irradiation, which leads to uncertainty in the power output. Forecast PV power generation is vital to improve grid stability and balance the energy supply and demand. This study aims to predict hourly day-ahead PV power generation by applying Temporal Fusion Transformer (TFT), a new attention-based architecture that incorporates an interpretable explanation of temporal dynamics and high-performance forecasting over multiple horizons. The proposed forecasting model has been trained and tested using data from six different facilities located in Germany and Australia. The results have been compared with other algorithms like Auto Regressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Multi-Layer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost), using statistical error indicators. The use of TFT has been shown to be more accurate than the rest of the algorithms to forecast PV generation in the aforementioned facilities. Full article

(This article belongs to the Special Issue Artificial Intelligence Techniques for Solar Irradiance and PV Modeling and Forecasting)

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22 pages, 8588 KiB

Open AccessArticle

A Horse Herd Optimization Algorithm (HOA)-Based MPPT Technique under Partial and Complex Partial Shading Conditions

by Sajid Sarwar, Muhammad Annas Hafeez, Muhammad Yaqoob Javed, Aamer Bilal Asghar and Krzysztof Ejsmont

Energies 2022, 15(5), 1880; https://doi.org/10.3390/en15051880 - 3 Mar 2022

Cited by 30 | Viewed by 4798

Abstract

The inconsistent irradiance, temperature, and unexpected behavior of the weather affect the output of photovoltaic (PV) systems, classified as partial or complex partial shading conditions. Under these circumstances, obtaining the maximum output power from PV systems becomes problematic. This paper proposes a population-based optimization model, the horse herd optimization algorithm (HOA), inspired by natural behavior, to solicit the maximum power under partial or complex partial shading conditions. It is an intelligent strategy inspired by the surprise pounce-chasing style of the horse herd model. The proposed technique outperforms the standard in different weather conditions, needs less computational time, and has a fast convergence speed and zero oscillations after reaching a power point’s maximum limit. A performance comparison of the HOA is achieved with conventional techniques, such as “perturb and observe” (P&O), the bio-inspired adaptive cuckoo search optimization (ACS), particle swarm optimization (PSO), and the dragonfly algorithm (DA). The following comparison of the presented scheme with the other techniques shows its better performance with respect to fast tracking and efficiency, as well as stability under disparate weather conditions and the ability to obtain maximum power with negligible oscillation under partial and complex shading. Full article

(This article belongs to the Special Issue Artificial Intelligence Techniques for Solar Irradiance and PV Modeling and Forecasting)

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16 pages, 9851 KiB

Open AccessArticle

A Parameter Estimation Method for a Photovoltaic Power Generation System Based on a Two-Diode Model

by Chao-Ming Huang, Shin-Ju Chen and Sung-Pei Yang

Energies 2022, 15(4), 1460; https://doi.org/10.3390/en15041460 - 16 Feb 2022

Cited by 10 | Viewed by 2341

Abstract

This study presents a parameter estimation method that uses an enhanced gray wolf optimizer (EGWO) to optimize the parameters for a two-diode photovoltaic (PV) power generation system. The proposed method consists of three stages. The first stage converts seven parameters for the two-diode model into 17 parameters for different environmental conditions, which provides more precise parameter estimation for the PV model. A PV power generation model is then established to represent the nonlinear relationship between inputs and outputs. The second stage involves a parameter sensitivity analysis and uses the overall effect method to remove the parameters that have smaller effect on the output. The final stage uses an enhanced GWO that is associated with measurement data to optimally estimate the parameters that are selected in the second stage. When the parameters are estimated, the predicted value for the PV power output is calculated for specific values of solar irradiation and module temperature. The proposed method is verified on a 200 kWp PV power generation system. To confirm the feasibility of the proposed method, the parameter estimation before and after optimization are compared, and these results are compared with other optimization algorithms, as well as those for a single-diode PV model. Full article

(This article belongs to the Special Issue Artificial Intelligence Techniques for Solar Irradiance and PV Modeling and Forecasting)

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