Optimization of photovoltaic modules tilt angle for Oman

Kazem, Hussein A.; Khatib, Tamer; Al-Waeli, Ali A Kazem

doi:10.1109/peoco.2013.6564637

Cited by 26 publications

(9 citation statements)

References 12 publications

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“…Two efficiencies need to be taken into consideration for this system, these are η inv and η wire inverter and wire efficiencies, respectively. η inv is calculated as follows 84 :

η_{inv} = ([], P_{in} ((), t) - P_{Loss} ((), t)) / P_{in} ((), t) η ((), t) = \frac{P_{in} ((), t) - P_{Loss} ((), t)}{P_{in} ((), t)},

where P in ( t ) and P Loss ( t ) are the input and losses, instantaneous and conversion powers, respectively.…”

Section: Modelling Of Photovoltaic Systems Output Currentmentioning

confidence: 99%

Evaluation ofPVoutput in terms of environmental impact based on mathematical and artificial neural network models

Kazem

2020

Int J Energy Res

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This paper presents different prediction models for a grid-connected photovoltaic (GCPV) system based on long-term data sets. A 1.4 kW GCPV installed in Sohar, Oman, where measurements of the electrical and environmental parameters are taken every second for 5 years (from May 2014 to April 2019) to reduce uncertainty and improve the proposed model's accuracy. The highest power and energy measured from the GCPV are 1180 W and 245.8 kWh, respectively. Mathematical regression and cascade-feed forward propagation (CFNN) models for PV current, voltage, and power were proposed in terms of environmental impact. A NeuroSolutions package was used to propose GCPV models for electrical quantities. An evaluation criterion is defined, in this study, to assess the system's performance. The proposed models show excellent agreement with the measured experimental data. However, CFNN shows a superior accuracy compared with empirical and regression proposed models. The two models have proposed a dispute, but the model relating the solar irradiation and ambient temperature to GCPV current is more accurate. To evaluate and validate the proposed CFNN models, mean square error, MAE, root mean square error, and R 2 metrics have been used as a criterion and compared with different artificial neural networks models in the literature. The proposed CFNN is found to give the most accurate results in terms of MSE = 0.0007, MAE = 0.4310, and RMSE = 0.0290 and highest accuracy R 2 = 0.9999, which shows the effectiveness and feasibility of the proposed models.

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“…Two efficiencies need to be taken into consideration for this system, these are η inv and η wire inverter and wire efficiencies, respectively. η inv is calculated as follows 84 :

η_{inv} = ([], P_{in} ((), t) - P_{Loss} ((), t)) / P_{in} ((), t) η ((), t) = \frac{P_{in} ((), t) - P_{Loss} ((), t)}{P_{in} ((), t)},

where P in ( t ) and P Loss ( t ) are the input and losses, instantaneous and conversion powers, respectively.…”

Section: Modelling Of Photovoltaic Systems Output Currentmentioning

confidence: 99%

Evaluation ofPVoutput in terms of environmental impact based on mathematical and artificial neural network models

Kazem

2020

Int J Energy Res

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“…This can be achieved by connecting it to a load. One a load is connected, current will flow in the circuit [12][13][14][15][16][17][18][19][20][21][22][23][24][25]. The PV panels only produces DC voltage, while most home appliances are AC voltage operated, this means that an inverter is needed to turn the Direct Current (DC) into Alternating Current (AC).…”

Section: Stand-alone Pv Systemsmentioning

confidence: 99%

Stand-alone PV systems for rural areas in Sabah, Malaysia: Review and case study application

Al‐Waeli¹,

Mahdi²

2017

IJOCAAS

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Solar energy is the energy utilized from the sun. Either to produce heat or electricity, solar energy is a clean source of energy. The electrical aspect of solar energy is produced using a semiconductor device named "Photovoltaic" or PV. The PV technology can be used to feed the grid, making it a grid connected system or to feed close by loads separately, making it a stand-alone system. This paper aims to study stand-alone PV systems in terms of: principle of work, construction and operation for rural areas in Malaysia. A case study application has been put forward and implemented in a village in Sipitang, Sabah, Malaysia. The system produced is a stand-alone PV system with 1 inverter, 1 charge controller, 2 batteries and 4 PV panels-all connected in parallel. capital cost analysis of the system has been conducted and found that the system costs 5,171.2 USD.

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“…The system specifications are as shown in Table 1. The system's PV modules are mounted at a fixed tilt angle of 27 deg facing true south in order to maximize the average annual power output as claimed by author [11]. The designed PV system is installed and put in service in January 2013.…”

Section: Pv System Descriptionmentioning

confidence: 99%

Modeling and Characterization of a Photovoltaic Array Based on Actual Performance Using Cascade-Forward Back Propagation Artificial Neural Network

Ameen

Pasupuleti

Khatib

et al. 2015

Journal of Solar Energy Engineering

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This paper proposes a novel prediction model for photovoltaic (PV) system output current. The proposed model is based on cascade-forward back propagation artificial neural network (CFNN) with two inputs and one output. The inputs are solar radiation and ambient temperature, while the output is output current. Two years of experimental data for a 1.4 kWp PV system are utilized in this research. The monitored performance is recorded every 2 s in order to consider the uncertainty of the system's output current. A comparison between the proposed model and other empirical and statistical models is done in this paper as well. Moreover, the ability of the proposed model to predict performance with high uncertainty rate is validated. Three statistical values are used to evaluate the accuracy of the proposed model, namely, mean absolute percentage error (MAPE), mean bias error (MBE), and root mean square error (RMSE). These values are used to measure the deviation between the actual and the predicted data in order to judge the accuracy of the proposed model. A simple estimation of the deviation between the measured value and the predicted value with respect to the measured value is first given by MAPE. After that, the average deviation of the predicted values from measured data is estimated by MBE in order to indicate the amount of the overestimation/underestimation in the predicted values. Third, the ability of predicting future records is validated by RMSE, which represents the variation of the predicted data around the measured data. Eventually, the percentage of MBE and RMSE is calculated with respect to the average value of the output current so as to present better understating of model's accuracy. The results show that the MAPE, MBE, and RMSE of the proposed model are 7.08%, À0.21 A (À4.98%), and 0.315 A (7.5%), respectively. In addition to that, the proposed model exceeds the other models in terms of prediction accuracy.

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Optimization of photovoltaic modules tilt angle for Oman

Cited by 26 publications

References 12 publications

Evaluation ofPVoutput in terms of environmental impact based on mathematical and artificial neural network models

Evaluation ofPVoutput in terms of environmental impact based on mathematical and artificial neural network models

Stand-alone PV systems for rural areas in Sabah, Malaysia: Review and case study application

Modeling and Characterization of a Photovoltaic Array Based on Actual Performance Using Cascade-Forward Back Propagation Artificial Neural Network

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