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Tested by the manufacturer, the electrical characteristics of PV modules are rated at the Standard Test Conditions (STC; 1000 W/m2 and 25 °C). But under real working conditions where the temperature and the irradiance are different from those of STC, the electrical specifications are definitely affected. Which in turn, affect the conversion efficiency and the Fill factor (FF). Here, the conversion efficiency and the FF of three different PV modules technologies: Poly-Crystalline Silicon (Poly C-Si), Copper Indium Gallium Selenide (CIGS) and Cadmium Telluride (CdTe) under STC and real working conditions of solar irradiance and temperature are evaluated based on real data, then analyzed. To this, a test facility is employed to carry out the required tests for the aforementioned PV technologies. The investigation presented in this paper aims to seek which PV technology is preferable in a specific level of irradiance and temperature.

L'irradiation horizontale globale prévue (GHI) peut aider à la conception, au dimensionnement et à l'analyse des performances des systèmes photovoltaïques (PV), y compris les systèmes de pompage PV à eau utilisés pour les applications d'irrigation. Dans cet article, divers modèles de réseaux neuronaux profonds (DNN) pour la prédiction à un jour du GHI à Hail city (Arabie saoudite) sont développés et étudiés. Les modèles DNN considérés comprennent la mémoire à long terme (LSTM), le LSTM bidirectionnel (BiLSTM), l'unité récurrente fermée (GRU), le GRU bidirectionnel (Bi-GRU), le réseau neuronal convolutionnel unidimensionnel (CNN 1D ) et d'autres configurations hybrides telles que CNN-LSTM et CNN-BiLSTM.A. L'ensemble de données des enregistrements quotidiens GHI collectés entre le 1er janvier 2000 et le 30 juin 2020 auprès de la National Aeronautics and Space Administration (NASA) à un endroit aride (Hail, Arabie Saoudite) est utilisé pour développer et comparer les modèles basés sur DNN ci-dessus. Les paramètres affectant la précision des modèles ont également été analysés en profondeur. Seules les valeurs historiques du GHI quotidien ont été utilisées pour construire les modèles basés sur DNN, tandis que des paramètres météorologiques supplémentaires tels que la température de l'air, la vitesse du vent, la direction du vent, la pression atmosphérique et l'humidité relative ne sont pas pris en compte dans ce travail. La bibliothèque Keras et le langage Python ont été utilisés utilisé pour développer et comparer les modèles de prévision GHI. Les métriques d'évaluation telles que le coefficient de corrélation (r), l'erreur absolue moyenne en pourcentage (MAPE), l'erreur absolue moyenne (MAE), la fonction de distribution cumulative (CDF) et l'écart type (σ ) sont choisies pour évaluer la performance des modèles de prévision. Les résultats obtenus ont montré que les modèles DNN ont fourni de bonnes performances à l'échelle mondiale avec une valeur maximale atteinte de r = 96 %, pour la prévision quotidienne GHI. La irradiación horizontal global pronosticada (GHI) puede ayudar a diseñar, dimensionar y analizar el rendimiento de los sistemas fotovoltaicos (PV), incluidos los sistemas de bombeo de agua PV utilizados para aplicaciones de riego. En este documento, se desarrollan e investigan varios modelos de redes neuronales profundas (DNN) para la predicción de un día de anticipación de GHI en la ciudad de Hail (Arabia Saudita). Los modelos DNN considerados incluyen memoria a largo plazo (LSTM), LSTM bidireccional (BiLSTM), unidad recurrente cerrada (GRU), GRU bidireccional (Bi-GRU), red neuronal convolucional unidimensional (CNN 1D ) y otras configuraciones híbridas como CNN-LSTM y CNN-BiLSTM. Un conjunto de datos de grabaciones diarias de GHI recopiladas durante el 1 de enero de 2000 al 30 de junio de 2020 de la Administración Nacional de Aeronáutica y del Espacio (NASA) en una ubicación árida (Hail, Arabia Saudita) se utiliza para desarrollar y comparar los modelos basados en DNN anteriores. Los parámetros que afectan la precisión de los modelos también se han analizado profundamente. Solo se han utilizado valores históricos de GHI diarios para construir los modelos basados en DNN, mientras que los parámetros climáticos adicionales como la temperatura del aire, la velocidad del viento, la dirección del viento, la presión atmosférica y la humedad relativa no se consideran en este trabajo. Biblioteca Keras y lenguaje Python han sido utilizado para desarrollar y comparar los modelos de pronóstico de GHI. Las métricas de evaluación como el coeficiente de correlación (r), el error porcentual absoluto medio (MAPE), el error absoluto medio (MAE), la función de distribución acumulativa (CDF) y la desviación estándar (σ ) se optan para evaluar el rendimiento de los modelos de predicción. Los resultados obtenidos mostraron que los modelos DNN han proporcionado un buen rendimiento a nivel mundial con un valor máximo alcanzado de r = 96%, para el pronóstico diario de GHI. Forecasted global horizontal irradiation (GHI) can help for designing, sizing and performances analysis of photovoltaic (PV) systems including water PV pumping systems used for irrigation applications.In this paper, various deep neural networks (DNN) models for one day-ahead prediction of GHI at Hail city (Saudi Arabia) are developed and investigated.The considered DNN models include long-shortterm memory (LSTM), bidirectional-LSTM (BiLSTM), gated recurrent unit (GRU), bidirectional-GRU (Bi-GRU), one-dimensional convolutional neural network (CNN 1D ) and other hybrid configurations such as CNN-LSTM and CNN-BiLSTM.A dataset of daily GHI recordings collected during January 1, 2000 to June 30, 2020 from National Aeronautics and Space Administration (NASA) at an arid location (Hail, Saudi Arabia) is used to develop and compare the above DNN-based models.The parameters affecting the accuracy of the models have been also deeply analyzed.Only historical values of daily GHI have been used to build the DNN-based models whereas additional weather parameters such as air temperature, wind speed, wind direction, atmospheric pressure and relative humidity are not considered in this work.Keras library and Python language have been used to develop and compare the GHI forecasting models.The evaluation metrics such as correlation coefficient (r), Mean Absolute Percent Error (MAPE), Mean Absolute Error (MAE), cumulative distribution function (CDF) and standard deviation (σ ) are opted to evaluate the performance of the prediction models.The obtained results showed that the DNN models have provided globally good performances with a maximum reached value of r = 96%, for daily GHI forecasting. يمكن أن يساعد الإشعاع الأفقي العالمي المتوقع (GHI) في تصميم وتحجيم وتحليل أداء الأنظمة الكهروضوئية (PV) بما في ذلك أنظمة ضخ المياه الكهروضوئية المستخدمة في تطبيقات الري. في هذه الورقة، يتم تطوير نماذج مختلفة للشبكات العصبية العميقة (DNN) للتنبؤ قبل يوم واحد من GHI في مدينة حائل (المملكة العربية السعودية) والتحقيق فيها. تشمل نماذج DNN التي يتم النظر فيها الذاكرة قصيرة المدى (LSTM)، ثنائية الاتجاه - LSTM (BiLSTM)، وحدة متكررة مسورة (GRU)، ثنائية الاتجاه - GRU (Bi - GRU)، شبكة عصبية التفافية أحادية البعد (CNN 1D ) والتكوينات الهجينة الأخرى مثل CNN - LSTM و CNN - BiLSTM. يتم استخدام مجموعة بيانات من تسجيلات GHI اليومية التي تم جمعها خلال الفترة من 1 يناير 2000 إلى 30 يونيو 2020 من الإدارة الوطنية للملاحة الجوية والفضاء (ناسا) في موقع قاحل (حائل، المملكة العربية السعودية) لتطوير ومقارنة النماذج القائمة على DNN المذكورة أعلاه. كما تم تحليل المعلمات التي تؤثر على دقة النماذج بعمق. تم استخدام القيم التاريخية فقط لـ GHI اليومية لبناء النماذج القائمة على DNN بينما لا يتم النظر في معلمات الطقس الإضافية مثل درجة حرارة الهواء وسرعة الرياح واتجاه الرياح والضغط الجوي والرطوبة النسبية في هذا العمل. تستخدم لتطوير ومقارنة نماذج التنبؤ بمؤشر GHI. يتم اختيار مقاييس التقييم مثل معامل الارتباط (r)، متوسط النسبة المئوية المطلقة (MAPE)، متوسط الخطأ المطلق (MAE)، وظيفة التوزيع التراكمي (CDF) والانحراف المعياري (σ ) لتقييم أداء نماذج التنبؤ. أظهرت النتائج التي تم الحصول عليها أن نماذج DNN قد قدمت أداءً جيدًا عالميًا بحد أقصى للقيمة التي تم الوصول إليها r = 96 ٪، للتنبؤ اليومي بمؤشر GHI.

Abstract In this paper, a Multiclass Adaptive Neuro-Fuzzy Classifier (MC-NFC) for fault detection and classification in photovoltaic (PV) array has been developed. Firstly, to show the generalization capability in the automatic faults classification of a PV array (PVA), Fuzzy Logic (FL) classifiers have been built based on experimental datasets. Subsequently, a novel classification system based on Adaptive Neuro-fuzzy Inference System (ANFIS) has been proposed to improve the generalization performance of the FL classifiers. The experiments have been conducted on the basis of collected data from a PVA to classify five kinds of faults. Results showed the advantages of using the fuzzy approach with reduced features over using the entire original chosen features. Then, the designed MC-NFC has been compared with an Artificial Neural Networks (ANN) classifier. Results demonstrated the superiority of the MC-NFC over the ANN-classifier and suggest that further improvements in terms of classification accuracy can be achieved by the proposed classification algorithm; furthermore faults can be also considered for discrimination.

AbstractAn implementation of an intelligent photovoltaic module on reconfigurable Field Programmable Gate Array (FPGA) is described in this paper. An experimental database of meteorological data (irradiation and temperature) and output electrical generation data of a Photovoltaic (PV) module (current and voltage) under variable climate condition is used in this study. Initially, an Artificial Neural Network (ANN) is developed under Matlab/Similuk, environment for modeling the PV module. The inputs of the ANN–PV module are the global solar irradiation and temperature while the outputs are the current and voltage generated from the PV‐module. Subsequently, the optimal configuration of the ANN model (ANN–PV module) is written and simulated under the Very High Description Language (VHDL) and ModelSim. The synthesized architecture by ModelSim is then implemented on an FPGA device. The designed MLP‐photovoltaic module permits the evaluation of performance of the PV module using only environmental parameters and involves less computational effort. The device can also be used for predicting the output electrical energy from the PV module and for a real time simulation in specific climatic conditions. Copyright © 2010 John Wiley & Sons, Ltd.

Abstract Growing of PV for electricity generation is one of the highest in the field of the renewable energies and this tendency is expected to continue in the next years. Due to the various seasonal, hourly and daily changes in climate, it is relatively difficult to find a suitable analytic model for predicting the performance of a grid-connected photovoltaic (GCPV) plant. In this paper, an artificial neural network is used for modelling and predicting the power produced by a 20 kW p GCPV plant installed on the roof top of the municipality of Trieste (latitude 45°40′N, longitude 13°46′E), Italy. An experimental database of climate (irradiance and air temperature) and electrical (power delivered to the grid) data from January 29th to May 25th 2009 has been used. Two ANN models have been developed and implemented on experimental climate and electrical data. The first one is a multivariate model based on the solar irradiance and the air temperature, while the second one is an univariate model which uses as input parameter only the solar irradiance. A database of 3437 patterns has been divided into two sets: the first (2989 patterns) is used for training the different ANN models, while the second (459 patterns) is used for testing and validating the proposed ANN models. Prediction performance measures such as correlation coefficient ( r ) and mean bias error (MBE) are presented. The results show that good effectiveness is obtained between the measured and predicted power produced by the 20 kW p GCPV plant. In fact, the found correlation coefficient is in the range 98–99%, while the mean bias error varies between 3.1% and 5.4%.

Abstract In this paper, various intelligent methods (IMs) used in tracking the maximum power point and their possible implementation into a reconfigurable field programmable gate array (FPGA) platform are presented and compared. The investigated IMs are neural networks (NN), fuzzy logic (FL), genetic algorithm (GA) and hybrid systems (e.g. neuro-fuzzy or ANFIS and fuzzy logic optimized by genetic algorithm). Initially, a complete simulation of the photovoltaic system with intelligent MPP tracking controllers using MATLAB/Simulink environment is given. Secondly, the different steps to design and implement the controllers into the FPGA are presented, and the best controller is tested in real-time co-simulation using FPGA Virtex 5. Finally, a comparative study has been carried out to show the effectiveness of the developed IMs in terms of accuracy, quick response (rapidity), flexibility, power consumption and simplicity of implementation. Results confirm the good tracking efficiency and rapid response of the different IMs under variable air temperature and solar irradiance conditions; however, the FL–GA controller outperforms the other ones. Furthermore, the possibility of implementation of the designed controllers into FPGA is demonstrated.

We compare the 24-hour ahead forecasting performance of two methods commonly used for the prediction of the power output of photovoltaic systems. Both methods are based on Artificial Neural Networks (ANN), which have been trained on the same dataset, thus enabling a much-needed homogeneous comparison currently lacking in the available literature. The dataset consists of an hourly series of simultaneous climatic and PV system parameters covering an entire year, and has been clustered to distinguish sunny from cloudy days and separately train the ANN. One forecasting method feeds only on the available dataset, while the other is a hybrid method as it relies upon the daily weather forecast. For sunny days, the first method shows a very good and stable prediction performance, with an almost constant Normalized Mean Absolute Error, NMAE%, in all cases (1% < NMAE% < 2%); the hybrid method shows an even better performance (NMAE% < 1%) for two of the days considered in this analysis, but overall a less stable performance (NMAE% > 2% and up to 5.3% for all the other cases). For cloudy days, the forecasting performance of both methods typically drops; the performance is rather stable for the method that does not use weather forecasts, while for the hybrid method it varies significantly for the days considered in the analysis.

To support farmers and improve the quality of crops production, designing of smart greenhouses is becoming indispensable. In this paper, a novel prototype for remote monitoring of a greenhouse is designed. The prototype allows creating an adequate artificial environment inside the greenhouse (e.g., water irrigation, ventilation, light intensity, and CO2 concentration). Thanks to the Internet of things technique, the parameters controlled (air temperature, relative humidity, capacitive soil moisture, light intensity, and CO2 concentration) were measured and uploaded to a designed webpage using appropriate sensors with a low-cost Wi-Fi module (NodeMCU V3). An Android mobile application was also developed using an A6 GSM module for notifying farmers (e.g., sending a warning message in case of any anomaly) regarding the state of the plants. A low-cost camera was used to collect and send images of the plants via the webpage for possible diseases identification and classification. In this context, a deep learning convolutional neural network was developed and implemented into a Raspberry Pi 4. To supply the prototype, a small-scale photovoltaic system was built. The experimental results showed the feasibility and demonstrated the ability of the prototype to monitor and control the greenhouse remotely, as well as to identify the state of the plants. The designed smart prototype can offer real-time remote measuring and sensing services to farmers.