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

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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

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