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(a) Polycrystalline panel, (b) monocrystalline panel, (c) flexible mono-panel, (d) thin-film amorphous panel, (e) CIGS solar panel, and (f) flexible back-contact mono-panel.
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This study presents a prediction model for comparing the performance of six different photovoltaic (PV) modules using artificial neural networks (ANNs), with simple inputs for the model. Cell temperature (Tc), irradiance, fill factor (FF), short circuit current (Isc), open-circuit voltage (Voc), maximum power (Pm), and the product of Voc and Isc ar...
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... this research, six different types of PV panels were used to collect data using a Prova 1011 solar system analyzer, namely, a polycrystalline panel (100 W), a monocrystalline panel (100 W), a flexible mono-panel (100 W), a thin-film amorphous panel (100 W), a CIGS solar panel (90 W), and a flexible back-contact mono-panel (30 W). Figure 1 shows the six PV modules' visual images while collecting the dataset. ...
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... test the accuracy of the proposed model under different test conditions, each predicted IV curve has a different solar radiation level and ambient temperature for each PV module. Figures 7-12 show the predicted IV curves with the experimental IV curves for the six modules that were extracted. For each predicted IV curve, the proposed model starts the process of training and predicting from the start, as shown in the flowchart in Figure 6. ...
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... this, to compare the prediction performance of the six PV modules under test conditions and how each PV module will perform under the same test conditions, the proposed model predicted the IV and PV curves for the six different types of PV modules under the same test conditions, as explained in Figure 6. Figures 13-16 compare and show the predicted IV and PV curves for the six modules under the same test conditions. All the graphs were created using the MATLAB program. ...
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... the graphs were created using the MATLAB program. From Figures 13-16, FF efficiency for the six modules under the same test conditions were calculated, as shown in Table 3. The highest FF is the monocrystalline module, with an average of 0.737, while the lowest FF is the CIGS module, with an average of 0.66. ...
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... thin-film and flexible mono-modules have similar performances. From Figures 13-16, FF efficiency for the six modules under the same test conditions were calculated, as shown in Table 3. The highest FF is the monocrystalline module, with an average of 0.737, while the lowest FF is the CIGS module, with an average of 0.66. ...
Citations
... This approach can predict the system performance, identify potential issues, and optimize configurations to ensure optimal energy returns and lasting stability. Specifically, the DT system comprises five key layers: the physical layer describes the tangible features of the photovoltaic array; the perception layer integrates data from meteorological stations and various sensors [22], providing real-time environmental factors for system design; the datatransmission layer manages this data and supports flexible transmission and storage strategies; the data-processing layer employs advanced algorithms, such as neural networks, for photovoltaic powergeneration predictions [23,24]; and the decision-making layer offers guidance on the best layout and grid-connection strategies based on these predictions. A schematic of the working principle of BIPV in the design phase is shown in Fig. 4. ...
Digital solutions, such as digital-twin (DT) technologies, can significantly improve the construction industry by addressing its inherent challenges, such as complex project management, delays, quality control, safety issues, and environmental impact. Building-integrated photovoltaics (BIPVs) is a promising system for building envelopes to harvest renewable solar energy onsite. Interest in the application of DT technologies in BIPV systems has grown, including the prediction and optimization of energy performance and accurate diagnosis of faults. Despite increased research on the integration of BIPV systems with DT technologies, a systematic, holistic analysis that encompasses all project phases for effective lifecycle management is still lacking. This study comprehensively analyzes the integration of BIPV systems and DT technology throughout the building lifecycle, including design, construction, operation, demolition, and recycling, and it underscores the potential of combining BIPV systems with DT technology as a strategic approach to enhance efficiency, safety, and reliability. This study investigates three primary objectives: 1) the current applications/attempts to apply DTs to BIPV systems from a lifecycle perspective; 2) the challenges of integrating DTs and BIPV; and 3) promising DT technologies/strategies for promoting BIPV development. This study offers significant insights by highlighting the use of DT technologies across the full lifecycle of BIPV systems. It details the application of these technologies across five crucial layers in BIPV systems, data technologies, modelling methods, and simulation techniques. Moreover, it elucidates current research gaps in this domain and proposes valuable recommendations for future research avenues.
... The photovoltaic (PV) system is made up of many parts, such as cells, wires, inverters, structures, and mechanical connections. The peak kilowatt represents the maximum amount of energy harvested from the system while the sun is overhead [4]. PV-based energy systems are popular among renewable energy sources because they are reliable, run quietly, are available all over the world, are easy to set up, and the cost of PV modules is going down [5]. ...
Photovoltaic (PV) system performance and reliability can be improved through the detection of defects in PV modules and the evaluation of their effects on system operation. In this paper, a novel system is proposed to detect and classify defects based on electroluminescence (EL) images. This system is called Fault Detection and Classification (FDC) and splits into four modules, which are (1) Image Preprocessing Module (IPM), (2) Feature Extraction Module (FEM), (3) Feature Selection Module (FSM), and (4) Classification Module (CM). In the first module (i.e., IPM), the EL images are preprocessed to enhance the quality of the images. Next, the two types of features in these images are extracted and fused together through FEM. Then, during FSM, the most important and informative features are extracted from these features using a new feature selection methodology, namely, Feature Selection-based Chaotic Map (FS-CM). FS-CM consists of two stages: filter stage using chi-square to initially select the most effective features and a modified selection stage using an enhanced version of Butterfly Optimization Algorithm (BOA). In fact, BOA is a popular swarm-based metaheuristic optimization algorithm that has only recently found success. While BOA has many benefits, it also has some drawbacks, including a smaller population and an increased likelihood of getting stuck in a local optimum. In this paper, a new methodology is proposed to improve the performance of BOA, called chaotic-based butterfly optimization algorithm. Finally, these selected features are used to feed the proposed classification model through CM. During CM, Hybrid Classification Model (HCM) is proposed. HCM consists of two stages, which are binary classification stage using Naïve Bayes (NB) and multi-class classification stage using enhanced multi-layer perceptron. According to the experimental results, the proposed system FDC outperforms the most recent methods. FDC introduced 98.2%, 89.23%, 87.2%, 87.9%, 87.55%, and 88.20% in terms of accuracy, precision, sensitivity, specificity, g-mean, and f-measure in the same order.
... In the former, data collected on the basis of historical time series for actual power generation from a PV facility were used directly to predict future PV production. In the latter method, solar irradiance and ambient temperature data were first predicted up to the target horizons and then the predicted values were used as inputs for a physical model of the PV facility, which related the solar irradiance and ambient temperature to the temperatures of the PV cells and the output power, in order to predict the generated power of the PV facility [14,15]. Furthermore, the latter method allowed the identification of different factors and parameters that improve the accuracy of the prediction models, including, but not limited to, the effect of scheduled maintenance/cleaning in large PV farms, detailed physical parameters of PV cells considering conversion efficiency and photonic characteristics [16]. ...
Nowcasting and forecasting solar irradiance are vital for the optimal prediction of grid-connected solar photovoltaic (PV) power plants. These plants face operational challenges and scheduling dispatch difficulties due to the fluctuating nature of their power output. As the generation capacity within the electric grid increases, accurately predicting this output becomes increasingly essential, especially given the random and non-linear characteristics of solar irradiance under variable weather conditions. This study presents a novel prediction method for solar irradiance, which is directly in correlation with PV power output, targeting both short-term and medium-term forecast horizons. Our proposed hybrid framework employs a fast trainable statistical learning technique based on the truncated-regularized kernel ridge regression model. The proposed method excels in forecasting solar irradiance, especially during highly intermittent weather periods. A key strength of our model is the incorporation of multiple historical weather parameters as inputs to generate accurate predictions of future solar irradiance values in its scalable framework. We evaluated the performance of our model using data sets from both cloudy and sunny days in Seattle and Medford, USA and compared it against three forecasting models: persistence, modified 24-hour persistence and least squares. Based on three widely accepted statistical performance metrics (root mean squared error, mean absolute error and coefficient of determination), our hybrid model demonstrated superior predictive accuracy in varying weather conditions and forecast horizons.
... Su et al. [8] applied machine learning techniques to study the large-lattice-mismatched CdS/CdTe interface. Jaber et al. [9], predicted the performance of different pv modules using artificial neural networks. Few studies exist in the literature on the effect of flexing photovoltaic on Ultrathin glass (see Ref. [5] and reference therein). ...
CdTe solar cells on ultra-thin glass substrates are light and flexible. Flexible cells are widely preferred modules in technological fields. The flexibility of these cells enables them to cope with deformations. The efficiency of these has reached 19%. In this work, we used artificial neural network (ANN) method for the determination the performance of flexible CdTe solar cells despite bending and time. The performances of the solar cell before and after bending have been predicted. According to the results from the ANN calculations using the experimental data in the literature, MSE values of ANN estimates range from 0.06% to 0.28%.
... The study conducted by Jaber et al. [30] presented a forecast model based on a generalized regression ANN to compare the performance of six photovoltaic modules. Using variables such as cell temperature, irradiance, fill factor, peak power, short circuit current, open circuit voltage, and the product of the last two variables, 37,144 records from 247 module curves were collected under various climatic conditions in Malaysia. ...
... In this bibliographical review, another significant aspect stands out: the influence of various factors on the generation of photovoltaic energy [24,[28][29][30][31][32][33]37]. These factors can be categorized into two groups: ...
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.
... They are easy to use and do not require advanced mathematical knowledge for problem-solving purposes. Compared to other methods, ANNs require less computational effort to establish a relationship between input parameters and targets (Benti et al., 2023;Garud et al., 2021;Jaber et al., 2022). ANNs are parallel information processing algorithms that are nonlinear and nonalgorithmic (Hoang et al., 2021;Jaber et al., 2022). ...
... Compared to other methods, ANNs require less computational effort to establish a relationship between input parameters and targets (Benti et al., 2023;Garud et al., 2021;Jaber et al., 2022). ANNs are parallel information processing algorithms that are nonlinear and nonalgorithmic (Hoang et al., 2021;Jaber et al., 2022). The basic unit of an ANN is a stack of neurons arranged in multiple layers. ...
... The basic unit of an ANN is a stack of neurons arranged in multiple layers. The input layer receives the data, the processing begins in the hidden layer, and the output layer generates the conclusion (Figure 3) (Jaber et al., 2022;Kurniawan & Harumwidiah, 2021). After adding bias, the input is multiplied by a weight. ...
The precise assessment and evaluation of global solar radiation (GSR) is crucial for designing effective solar energy systems. However, in developing countries like Ethiopia, the cost and maintenance of measuring devices are inadequate. As a result, researchers have explored alternative methods such as empirical models to estimate GSR. This article proposes using artificial neural networks (ANN) to predict daily and monthly averaged horizontal GSR (HGSR) around Fiche town of Ethiopia, using various network types. The input variables were divided into training (70%) and testing (30%) sets to evaluate the network types, with the sigmoid function used as the activation function at the hidden layer and a linear function for the output layer. The predicted mean daily and monthly HGSR ranges from 3.282 kWh/m²/day to 6.967 kWh/m²/day and 4.628 kWh/m² to 6.613 kWh/m² respectively. The values obtained were compared to those provided by NASA observation data and were found to be within acceptable limits. Statistical metrics of MAPE, MSE, and RMSE show that CFBP, FFBP, LR, and EBP are better network types for estimating mean daily HGSR, while EBP, FFBP, CFBP, and LR are better for estimating mean monthly HGSR. Overall, all network types of ANN accurately predicted the mean daily and monthly HGSR. In general, the findings of this study indicated that the location had promising solar energy for producing electricity and for various uses.
... As a result of exploiting experimental data from 8 years of operation, Roumpakias et al. applied a common type of ANN for the long-term prediction of a 100 kW grid-connected PV park's output [44]. Using artificial neural networks (ANN), Jaber et al. compared the performance of six different photovoltaic (PV) modules with actual measurements [45]. The investigations available allow designers to preliminary predict the performance of PV systems and manage the PV energy use in the best possible way for that application considered. ...
Nowadays, special attention is paid to the importance of using photovoltaic (PV) systems to tackle the problem of climate change and the energy crisis. Artificial intelligence is currently used in different science fields for its great potential and accuracy in forecasting problems. In this work, a network of artificial neural networks (ANNs) was trained and validated to forecast the hourly worldwide electrical power produced by various PV modules, with different electrical characteristics. Each ANN describes the worldwide performance of each PV module on the optimal inclination angle. The training data consists of the hourly air temperature, horizontal total solar radiation as input data and electrical power produced as output. The power is obtained from the hourly simulation of PV modules with an electrical circuit model in 24 localities at very different latitudes. The validation and generalization of the network were obtained by considering the six PV modules in further 24 localities and by considering two further PV modules in all 48 localities considered. The excellent results in terms of accuracy metrics confirmed that the network of ANNs is a reliable, simple and accurate tool that can be used to predict the hourly performance of any PV module in any location worldwide.
... In the work of Jaber et al. [28], a forecast model is described to compare the performance of six different photovoltaic modules using ANNs, which corresponds to a generalized regression neural network (GRNN). As inputs to the model, the following were used: cell temperature, irradiance, fill factor, maximum power, short-circuit current (ISC), open-circuit voltage (VOC), and the product of these last two variables (VOC and ISC). ...
... Additionally, research works dedicated to studying the effects of meteorological conditions on the internal components of photovoltaic modules were found, which can also affect the performance of forecast models of photovoltaic energy production ( [27][28][29][30][31]). In this same direction, other works point to physical aspects of the manufacture of batteries for photovoltaic energy storage, pointing out its importance to be considered in hybrid models and thus improving forecasting capabilities. ...
In recent years, photovoltaic energy has become one of the most implemented electricity generation options to help reduce environmental pollution suffered by the planet. Accuracy in this photovoltaic energy forecasting is essential to increase the amount of renewable energy that can be introduced to existing electrical grid systems. The objective of this work is based on developing various computational models capable of making short-term forecasting about the generation of photovoltaic energy that is generated in a solar plant. For the implementation of these models, a hybrid architecture based on recurrent neural networks (RNN) with long short-term memory (LSTM) or gated recurrent units (GRU) structure, combined with shallow artificial neural networks (ANN) with multilayer perceptron (MLP) structure, is established. RNN models have a particular configuration that makes them efficient for processing ordered data in time series. The results of this work have been obtained through controlled experiments with different configurations of its hyperparameters for hybrid RNN-ANN models. From these, the three models with the best performance are selected, and after a comparative analysis between them, the forecasting of photovoltaic energy production for the next few hours can be determined with a determination coefficient of 0.97 and root mean square error (RMSE) of 0.17. It is concluded that the proposed and implemented models are functional and capable of predicting with a high level of accuracy the photovoltaic energy production of the solar plant, based on historical data on photovoltaic energy production.
... The use of loads that are not constant allows to analyze the points in which the sources operate that allow drawing the graphs that relate the current and voltage (curve I-V). This is achieved by varying the current between the values corresponding to the short circuit (ISC) and open circuit values (VOC) [27], [28]. The procedure described above is usually conducted with this variable load that can be found in special equipment, which also has analog signal acquisition, data processing and control stages. ...
The rapid growth of the market for the use of renewable energy has increased
the use of solar energy which has a significant role in power generation. This
requires the insertion of equipment capable of providing precise
measurements of the photovoltaic modules, either to verify the operation of
the installation or to find specific problems. In this scenario, the current versus
voltage curve tracer is used to describe the electrical behavior of the
photovoltaic system through all the operating possibilities, but it has an
excessive cost for small installations.
... PV network's accuracy is improved by increasing the various hidden nodes, although doing so complicates the computation. Because of this, ANN-based techniques are less effective for controllers with low costs and slow processing speeds [6]. ...
In contrast to typical designs, an Interleaved Boost Converter (IBC) is created for Solar Powered E-vehicles (SPEVs) uses that has fewer input/output filters, a quicker dynamic characteristic, and less device stress. A SPEV's multi-source energy-storage system connects the IBC to the DC-bus and PV’s output. The size of passive elements and also the fluctuations in the output voltage and input voltage are often reduced with great efficiency using an IBC’s current control loop. In order to ensure a UPS, support the vehicle's movement in transient conditions, and recharge the battery via regenerative braking, a PV-Battery system must use MPPT controller. PV systems run well under optimal conditions, however shading, irradiation, temperature, and inclination angle can may the PV system into a partial shading situation. Several power peaks occur under these situations, making it difficult to determine the global peak. A solar system's MPPT approach tracks and maintains the MPP to prevent variations. This study presents a novel Water Cycle Optimized Perturb and Observe (WCO-PO) algorithm to solve the issues. In various weather circumstances in MATLAB/Simulink Software, the proposed technique demonstrated reduced computing time, quick convergence rate, and negligible fluctuations after attaining MPP. A comparative analysis of the proposed and other methods is analyzed and these test results are provided.
