Experimental and deep learning artificial neural network approach for evaluating grid-connected photovoltaic systems

Kazem, Hussein A.; Yousif, Jabar H.; Chaichan, Miqdam T.; Al‐Waeli, Ali H.A.

doi:10.1002/er.4855

Cited by 53 publications

(23 citation statements)

References 46 publications

(67 reference statements)

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“…The solar radiation and ambient temperature are found influential parameters for electrical efficiency and only ambient temperature is found influential parameter for thermal efficiency based on sensitivity analysis 116 . Kazem et al have proposed four neural network models to forecast the PV current of a 1.4 kW building integrated grid connected photovoltaic power plant installed in Oman 117 . Al‐Waeli et al have formulated an ANN model to forecast the electrical performance of photovoltaic/thermal systems with R 2 of 0.81 and RMSE of 0.371 118 .…”

Section: Application Of Ai Techniques In Solar Photovoltaic Systemsmentioning

confidence: 99%

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A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models

2020

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The uncertainty associated with modeling and performance prediction of solar photovoltaic systems could be easily and efficiently solved by artificial intelligence techniques. During the past decade of 2009 to 2019, artificial neural network (ANN), fuzzy logic (FL), genetic algorithm (GA) and their hybrid models are found potential artificial intelligence tools for performance prediction and modeling of solar photovoltaic systems. In addition, during this decade there is no extensive review on applicability of ANN, FL, GA and their hybrid models for performance prediction and modeling of solar photovoltaic systems. Therefore, this article focuses on extensive review on design, modeling, maximum power point tracking, fault detection and output power/efficiency prediction of solar photovoltaic systems using artificial intelligence techniques of the ANN, FL, GA and their hybrid models. In addition, the selected articles on the solar radiation prediction using ANN, FL, GA and their hybrid models are also summarized. Total of 122 articles are reviewed and summarized in the present review for the period of 2009 to 2019 with 90 articles in the field of {ANN, FL, GA and their hybrid models} + solar photovoltaic systems and 32 articles in the field of {ANN, FL, GA and their hybrid models} + solar radiation. The review shows the suitability and reliability of ANN, FL, GA and hybrid models for accurate prediction of the solar radiation and the performance characteristics of solar photovoltaic systems. In addition, this review presents the guidance for the researchers and engineers in the field of solar photovoltaic systems to select the suitable prediction tool for enhancement of the performance characteristics of the solar photovoltaic systems and the utilization of the available solar radiation.

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Section: Application Of Ai Techniques In Solar Photovoltaic Systemsmentioning

confidence: 99%

“…Kazem et al have proposed four neural network models to forecast the PV current of a 1.4 kW building integrated grid connected photovoltaic power plant installed in Oman 117. Al-Waeli et al have formulated an ANN model to forecast the electrical performance of photovoltaic/thermal systems with R 2 of 0.81 and RMSE of 0.371 118.…”

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confidence: 99%

A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models

2020

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“…Sohar is one of the northern provinces of Al-Batinah and is located in the northern part of Oman. Also, Sohar is 234 km north of the capital Muscat [ 31 ]. Figure 2 shows ambient temperature, diffuse and global solar irradiance.…”

Section: Methodology and Experimental Setupmentioning

confidence: 99%

“…This system is installed in the Sohar University campus, for scientific research purposes. The PV modules are installed at a fixed optimum tilt angle (27 facing southward depending on Kazem et al results) [31]. The PV modules are connected in series to supply sufficient voltage to the grid-connected inverter (the ten connected PV modules provided a series of 177 V).…”

Section: Pv System Descriptionmentioning

confidence: 99%

Long-term power forecasting using FRNN and PCA models for calculating output parameters in solar photovoltaic generation

Kazem

Yousif

Chaichan

et al. 2022

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This paper evaluated a 1.4 kW grid-connected photovoltaic system (GCPV) using two neural network models based on experimental data for one year. The novelty of this study is to propose and compare full recurrent neural network (FRNN), and principal component analysis (PCA) models based on entire year experimental data, considering limited research conducted to predict GCPV behaviour using the two methods. The system data was collected for 12 months secondly and hourly data with 50400 samples daily. The GCPV evaluates using specific yield, energy cost, capacity factor, payback period, current, voltage, power, and efficiency. The predicted GCPV current and power using FRNN and PCA were evaluated and compared with measured values to validate results. However, the results indicated that FRNN is better in simulating the experimental results curve compared with PCA. The measured and predicted data are compared and evaluated. It is found that the GCPV is suitable and promising for the study area in terms of technical and economic evaluation with a 3.24–4.82 kWh/kWp-day yield, 21.7% capacity factor, 0.045 USD/kWh cost of energy, and 11.17 years payback period.

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“…Hence, there are many successful applications of these solutions to predict PV and PVT performance, in addition to predicting energy requirements in buildings . Kazem et al used the data of experiments carried out for 1 year for a 1.4‐kW grid‐connected PVT installed in Faculty of Engineering building to create four predictive models using a deep learning algorithm. The input data were ambient temperature and solar irradiance and the evaluation was made based on system current output.…”

Section: Introductionmentioning

confidence: 99%

Machine learning predictive models for optimal design of building‐integrated photovoltaic‐thermal collectors

et al. 2020

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This research article aims to examine the feasibility of several machine learning techniques to forecast the exergetic performance of a building-integrated photovoltaic-thermal (BIPVT) collector. In this regard, it uses multiple linear regression, multilayer perceptron, radial basis function regressor, sequential minimal optimization improved support vector machine, lazy.IBK, random forest (RF), and random tree approaches. Moreover, it implements the performance evaluation criteria (PEC) to evaluate the system's performance from the perspective of exergy. The use of these approaches serves the identification process to realize the relationship between the input-output parameters of the BIPVT system. The novelty of this work is that it utilizes and compares multiple learning algorithms to predict the PEC of BIPVT through design parameters. Hence, the research considers the parameter (PEC) as the essential output of the BIPVT collector, while the input parameters are the length, width, and depth of the duct, located under the PV modules, as well as the air mass flow rate. The results of the research for the statistical indexes of mean absolute error, root mean square error, relative absolute error (%), and root relative squared error (%) show values of (0.2967, 0.3885, 1.8754, and 1.5237) and (0.4957, 0.8153, 2.9586, and 2.8289), respectively, for the training and testing datasets. While R 2 ranges (0.9997-0.9999) for those datasets. Therefore, to estimate the exergy performance of the BIPVT collector, the RF model is superior to other proposed models. K E Y W O R D SBIPVT, exergetic performance, machine learning, performance evaluation criteria, predictive model

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Experimental and deep learning artificial neural network approach for evaluating grid-connected photovoltaic systems

Cited by 53 publications

References 46 publications

A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models

A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models

Long-term power forecasting using FRNN and PCA models for calculating output parameters in solar photovoltaic generation

Machine learning predictive models for optimal design of building‐integrated photovoltaic‐thermal collectors

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