Development Model of the Inverter Size for the GCC Countries

Isa Salman Qamber

Abstract

It is important to develop a suitable model to find the suitable design of inverter based on a required specification that needed to calculate the number of units per day requested by the house holders or companies. The present study deals with the required design using a developed Adaptive Neuro-Fuzzy Inference System (ANFIS) method and based on the calculated results the curve fitting is applied to form the suitable formula. The model is formulated as a function of number of units per day for the Gulf Cooperation Council (GCC) region. In general, the GCC region has high-energy consumption influenced by several factors. Therefore, the solar energy is helping to reduce the use of gas or other fuels to generate power. This means that the obtained results will encourage the GCC through the energy field development and setting the future for it to increase the use of solar energy production. The novelty of the present study is to avoid an increase in generation capacity using the gas to produce the electricity. In addition, it will help the GCC countries to avoid load shedding and meet the energy demands in different sectors. Furthermore, the developed model will help the economic development of the GCC countries. These results reduce capital investment, limiting the equipment installed and the expected load needed in the region.

Keywords

ANFIS; Curve fitting; Panel generation factor; Photovoltaic.

Article Metrics

Abstract view : 231 times
PDF - 136 times

Full Text:

PDF

References

I. S. Qamber and M. Y. Al-Hamad, Photovoltaic design for smart cities and demand forecasting using a truncated conjugate gradient algorithm, in Optimization, Learning, and Control for Interdependent Complex Networks, 1123, M. H. Amini Ed. Cham: Springer International Publishing, 2020, 277-295.

M. T. Mitoa, X. Maa, H. Albuflasab and P. A. Davies, Reverse osmosis (RO) membrane desalination driven by wind and solar photovoltaic (PV) energy: State of the art and challenges for large-scale implementation, Renewable and Sustainable Energy Reviews, 112, 2019, 669–685.

I. S. Qamber and M. Y. Al-Hamad, Novel PV panels design modeling to support smart cities, International Journal of Computing and Digital Systems, 8(2), 2019, 125-130.

M. Albadi, H. Soliman, M. Thani, A. Al-Alawi, S. Al-Ismaili, A. Al-Nabhani and H. Baalawi, Optimal allocation of PV systems to minimize losses in distribution networks using GA and PSO: Masirah Island case study, Journal Electrical Systems, 13(4), 2017, 678-688.

M. Y. Al-Hamad and I. S. Qamber, Smart PV grid to reinforce the electrical network, Proceedings of the 17th World Renewable Energy Congress, Manama, 2016, 01002.

I. M. Marchena, M. S. Cardona and L. M. Lopez, Framework for monitoring and assessing small and medium solar energy plants, Journal of Solar Energy Engineering, 137(2), 2015, 020017.

G. D. Santika, W. F. Mahmudy and A. Naba, Electrical load forecasting using adaptive neuro-fuzzy inference system, International Journal of Advances in Soft Computing and Its Applications, 9(1), 2017, 50–69.

S. Saravanan, S. Kannan, C. Thangaraj, Prediction of India’s electricity demand using ANFIS, ICTACT Journal on Soft Computing, 5(3), 2015, 985–990.

M. Y. AL-Hamad and I. S. Qamber, GCC electrical long-term peak load forecasting modeling using ANFIS and MLR methods, International Arab Journal of Basic and Applied Sciences, 26(1), 2019, 269–282.

Leonics Support, How to Design Solar PV System: What is solar PV system?, Energy Conservation Guide, http://www.leonics.com/support/article2_12j/articles2_12j_en.php, 2019.

Refbacks

  • There are currently no refbacks.