Modeling of photovoltaic array output current based on actual performance using artificial neural networks

Ameen, Ammar Mohammed; Pasupuleti, Jagadeesh; Khatib, Tamer

doi:10.1063/1.4931464

Cited by 6 publications

(6 citation statements)

References 23 publications

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“…Therefore, if the number of neurons is undersized, the results will be underfitting, causing high training and high generalization error. While if the number of neurons is oversized, then the results will be overfitting, and a high variance may occur [17].…”

Section: Introductionmentioning

confidence: 99%

Predicting the Output Power of a Photovoltaic Module Using an Optimized Offline Cascade-Forward Neural Network-Based on Genetic Algorithm Model

Turki

Shammari

2021

Technol Econ Smart Grids Sustain Energy

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This paper aims to enhance the performance of a cascade-forward neural network (CFNN) model to predict the output power of a photovoltaic (PV) module. This improvement is conducted by optimizing the number of hidden neurons using the genetic algorithm (GA). The optimization is carried out to minimize the value of the root mean square error (RMSE) between the actual and predicted PV output power. The performance of the CFNN-based GA is evaluated using five statistical term error terms; namely, RMSE, normalized RMSE (nRMSE), mean bias error (MBE), normalized MBE (nMBE), and mean absolute percentage error (MAPE). The values for these terms are 0.0025 W, 0.0131%, 0.0011 W, 0.0067% and 0.8121%, respectively. The proposed model's performance is compared with that obtained using the classical CFNN, classical feed-forward neural network (FFNN), and FFNN-based on GA in accuracy training and testing time. The results show that the proposed model performs better than the models mentioned above based on the statistical error terms. The MAPE of the proposed model is 0.8121%, which is lower by 98.63%, 98.77% and 63.93% than that obtained by FFNF, CFNN, and FFNN-GA, respectively. Besides, it is faster than the models mentioned above based on training and testing time. In which, the proposed model is faster than FFNF, CFNN, and FFNN-GA by 89.68%, 59.50% and 53.99% in terms of training time, respectively. Moreover, it is also faster in testing than FFNN, CFNN, and FFNN-GA by 94.92%, 75.57% and 78.95%, respectively. Accordingly, the proposed model can be recommended to predict the output power of the PV modules.

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Section: Introductionmentioning

confidence: 99%

Predicting the Output Power of a Photovoltaic Module Using an Optimized Offline Cascade-Forward Neural Network-Based on Genetic Algorithm Model

Turki

Shammari

2021

Technol Econ Smart Grids Sustain Energy

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“…After that, a comparison between the proposed models was done so as to pick out the best model based on prediction accuracy. More examples of ANNs based models for PV system output power prediction can be found in (Ameen et al, 2015;Chow et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…In all previous ANN based models (Ameen et al, 2015;Brano et al, 2014;Chow et al, 2012;Sulaiman et al, 2012), the prediction process is accurate in a very good way, however, models accuracy is not everything to consider when comparing these models. In most of these models (Ameen et al, 2015;Brano et al, 2014;Chow et al, 2012;Sulaiman et al, 2012), the authors have used large dataset (at least for one year time) to train the models. These datasets were mostly hourly datasets but in sometimes these datasets were in seconds such as in (Ameen et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Novel models for photovoltaic output current prediction based on short and uncertain dataset by using deep learning machines

Khatib

Gharaba

Hamad

et al. 2021

Energy Exploration & Exploitation

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This paper presents deep learning neural network models for photovoltaic output current prediction. The proposed models are long short-term memory and gated recurrent unit neural networks. The proposed models can predict photovoltaic output current for each second for a week time by using global solar radiation and ambient temperature values as inputs. These models can predict the output current of the photovoltaic system for the upcoming seven days after being trained by half-day data only. Python environment is used to develop the proposed models, and experimental data of a 1.4 kWp PV system are used to train, validate and test the proposed models. Highly uncertain data with steps in seconds are used in this research. Results show that the proposed models can accurately predict photovoltaic output current whereas the average values of the root mean square error of the predicted values by the proposed LSTM and GRU are 0.28 A and 0.27 A (the maximum current of the system is 7.91 A). In addition, results show that GRU is slightly more accurate than LSTM for this purpose and utilises less processor capacity. Finally, a comparison with other similar methods is conducted so as to show the significance of the proposed models.

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“…Moreover, the accuracy of the PV module models is strongly affected by the way of extracting their unknown parameters. Several research works discussed the topic of PV model parameters estimation, by applying different methods based on analytical [11], numerical [12,13] and bio-inspired optimization solution [14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

“…In order to make the obtained results more comprehensive, other machines learning used for modeling the DC output current of PV arrays were considered. Ameen et al[13] reported RMSE of 5.67% in a work based on artificial neural networks for forecasting the output current of a PV array. Ibrahim et al[38] published a novel machine learning consisting in using random forests technique for modeling the output current of a PV array, the RMSE provided is of 2.74%.…”

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confidence: 99%

Comparison of two PV array models for the simulation of PV systems using five different algorithms for the parameters identification

et al. 2016

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Simulation is of primal importance in the prediction of the produced power and automatic fault detection in PV grid-connected systems (PVGCS). The accuracy of simulation results depends on the models used for main components of the PV system, especially for the PV module. The present paper compares two PV array models, the five-parameter model (5PM) and the Sandia Array Performance Model (SAPM). Five different algorithms are used for estimating the unknown parameters of both PV models in order to see how they affect the accuracy of simulations in reproducing the outdoor behavior of three PVGCS. The arrays of the PVGCS are of three different PV module technologies: Crystalline silicon (c-Si), amorphous silicon (a-Si:H) and micromorph silicon (a-Si:H/µc-Si:H).\ud \ud The accuracy of PV module models based on the five algorithms is evaluated by means of the Route Mean Square Error (RMSE) and the Normalized Mean Absolute Error (NMAE), calculated for different weather conditions (clear sky, semi-cloudy and cloudy days). For both models considered in this study, the best accuracy is obtained from simulations using the estimated values of unknown parameters delivered by the ABC algorithm. Where, the maximum error values of RMSE and NMAE stay below 6.61% and 2.66% respectively.Peer ReviewedPostprint (author's final draft

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Modeling of photovoltaic array output current based on actual performance using artificial neural networks

Cited by 6 publications

References 23 publications

Predicting the Output Power of a Photovoltaic Module Using an Optimized Offline Cascade-Forward Neural Network-Based on Genetic Algorithm Model

Predicting the Output Power of a Photovoltaic Module Using an Optimized Offline Cascade-Forward Neural Network-Based on Genetic Algorithm Model

Novel models for photovoltaic output current prediction based on short and uncertain dataset by using deep learning machines

Comparison of two PV array models for the simulation of PV systems using five different algorithms for the parameters identification

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