Determination of the Appropriate Number of Photovoltaic Panels for Microgeneration and Self-supply of Final Consumers by Energy Production Estimation via Fuzzy Logic

Cristhian Chávez, Juan David Ramírez, María Fernanda Trujillo L., Patricia Otero, Sebastián Taco-Vásquez, Víctor Tibanlombo

Abstract


A method is presented to determine the appropriate number of photovoltaic panels that should be installed in an end-user photovoltaic installation to guarantee the supply of energy to the load during the hours of solar radiation, according to factors such as the installation area and global solar radiation. Solar radiation is predicted by approximating the daily distribution of global irradiance through a Gaussian function, which is subsequently corrected using a heuristic approach. Meteorological parameters are used as input data such as the daily solar insolation and the maximum global irradiance for each day; this last parameter is obtained through an expert system based on fuzzy logic that was programmed and trained with the data of ambient temperature and relative humidity that were obtained in the processing stage. Output from this expert system is the predicted values of maximum radiation obtained for each day for a selectable time interval. With the predicted solar radiation, the generation of electrical energy from the photovoltaic panels is calculated. The load is randomly modeled from a pattern of the energy demand of the building to be powered by the photovoltaic system. The number of photovoltaic panels needed is found with the information acquired in the previous stages and the information of the energy demand of the load and the installation area. The results are the number of solar panels that would be needed at all hours of the day from which the radiation prediction was made.

Keywords


Fuzzy logic; prediction of solar radiation; photovoltaic systems; capital recovery time; energy production estimation.

Full Text:

PDF

References


Arconel, “Generación fotovoltaica para autoabastecimiento de consumidores finales de energía eléctrica,” 2018. .

Arconel, “Pliego Tarifario Para Las Empresas Eléctricas De Distribución,” 2020. .

F. Camilo, R. Castro, M. Almeida, V. P.-S. Energy, and undefined 2017, “Economic assessment of residential PV systems with self-consumption and storage in Portugal,” Elsevier.

S. Karjalainen, H. A.-R. energy, and undefined 2019, “Pleasure is the profit-The adoption of solar PV systems by households in Finland,” Elsevier.

J. Al-Saqlawi, K. Madani, N. M. D.-E. C. and, and undefined 2018, “Techno-economic feasibility of grid-independent residential roof-top solar PV systems in Muscat, Oman,” Elsevier.

A. Duman, Ö. G.-R. Energy, and undefined 2020, “Economic analysis of grid-connected residential rooftop PV systems in Turkey,” Elsevier.

P. L. Zervas, H. Sarimveis, J. A. Palyvos, and N. C. G. Markatos, “Prediction of daily global solar irradiance on horizontal surfaces based on neural-network techniques,” Renew. Energy, vol. 33, no. 8, pp. 1796–1803, 2008.

H. Zhang, T.-W. Weng, P.-Y. Chen, C.-J. Hsieh, and L. Daniel, “Efficient Neural Network Robustness Certification with General Activation Functions,” Adv. Neural Inf. Process. Syst., vol. 2018-December, pp. 4939–4948, Nov. 2018.

F. Ghasemi, A. Mehridehnavi, A. Pérez-Garrido, and H. Pérez-Sánchez, “Neural network and deep-learning algorithms used in QSAR studies: merits and drawbacks,” Drug Discovery Today, vol. 23, no. 10. Elsevier Ltd, pp. 1784–1790, Oct. 2018, doi: 10.1016/j.drudis.2018.06.016.

H. Wang, R. Czerminski, and A. C. Jamieson, “Neural Networks and Deep Learning,” in The Machine Age of Customer Insight, Emerald Publishing Limited, 2021, pp. 91–101.

H. Sarimveis, A. Alexandridis, G. Tsekouras, and G. Bafas, “A fast and efficient algorithm for training radial basis function neural networks based on a fuzzy partition of the input space,” Ind. Eng. Chem. Res., vol. 41, no. 4, pp. 751–759, 2002.

E. H. C. Harik, F. Guérin, F. Guinand, J. F. Brethé, H. Pelvillain, and J. Y. Parédé, “Fuzzy logic controller for predictive vision-based target tracking with an unmanned aerial vehicle,” Adv. Robot., vol. 31, no. 7, pp. 368–381, Apr. 2017, doi: 10.1080/01691864.2016.1271500.

A. J. Guimarães, V. J. Silva Araujo, P. V. de Campos Souza, V. S. Araujo, and T. S. Rezende, “Using fuzzy neural networks to the prediction of improvement in expert systems for treatment of immunotherapy,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Nov. 2018, vol. 11238 LNAI, pp. 229–240, doi: 10.1007/978-3-030-03928-8_19.

Z. Pezeshki and S. M. Mazinani, “Comparison of artificial neural networks, fuzzy logic and neuro fuzzy for predicting optimization of building thermal consumption: a survey,” Artificial Intelligence Review, vol. 52, no. 1. Springer Netherlands, pp. 495–525, Jun. 2019, doi: 10.1007/s10462-018-9630-6.

S. Jahedi Rad, M. Kaveh, V. R. Sharabiani, and E. Taghinezhad, “Fuzzy logic, artificial neural network and mathematical model for prediction of white mulberry drying kinetics,” Heat Mass Transf. und Stoffuebertragung, vol. 54, no. 11, pp. 3361–3374, Nov. 2018, doi: 10.1007/s00231-018-2377-4.

M. S. Mahmoud, Fuzzy control, estimation and diagnosis: Single and interconnected systems. Springer International Publishing, 2017.

S. Sakunthala, R. Kiranmayi, and P. N. Mandadi, “A review on artificial intelligence techniques in electrical drives: Neural networks, fuzzy logic, and genetic algorithm,” in Proceedings of the 2017 International Conference On Smart Technology for Smart Nation, SmartTechCon 2017, May 2018, pp. 11–16, doi: 10.1109/SmartTechCon.2017.8358335.

R. Jafari, M. A. Contreras, W. Yu, and A. Gegov, “Applications of Fuzzy Logic, Artificial Neural Network and Neuro-Fuzzy in Industrial Engineering,” Mech. Mach. Sci., vol. 86, pp. 9–14, 2020, doi: 10.1007/978-3-030-45402-9_2.

Secretaría de Ambiente de Quito, “Sistema de Información Ambiental Distrital.” .

L. X. Wang and J. M. Mendel, “Generating fuzzy rules by learning from examples,” IEEE Trans. Syst. Man. Cybern., vol. 22, no. 6, pp. 1414–1427, 1992.

H. L. Tsai, C. S. Tu, and Y. J. Su, “Development of generalized photovoltaic model using MATLAB/SIMULINK,” in Proceedings of the world congress on Engineering and computer science, 2008, vol. 2008, pp. 1–6.




DOI: http://dx.doi.org/10.18517/ijaseit.12.2.15291

Refbacks

  • There are currently no refbacks.



Published by INSIGHT - Indonesian Society for Knowledge and Human Development