Solar radiation analyzing by neuro-fuzzy approach

Energy and Buildings

Volumn 129, Issue , 2016, Pages 261-263

  Jović, Srđan ^a   Aničić, Obrad ^a   Marsenić, Mladen ^a   Nedić, Bogdan ^b  

^a UNIVERSITY OF PRI TINA   (Serbia)

^b UNIVERSITY OF KRAGUJEVAC   (Serbia)

Author keywords

ANFIS; Prediction; Solar radiation

Indexed keywords

FORECASTING; FUZZY NEURAL NETWORKS; FUZZY SYSTEMS; RADIATION; SEA LEVEL; SOLAR ENERGY; SOLAR RADIATION;

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM; ALTERNATIVE ENERGY; ANFIS; DRY BULB TEMPERATURE; NEURO-FUZZY APPROACH; SOLAR RADIATION PREDICTIONS; VARIABLE SELECTION; WET BULB TEMPERATURE;

FUZZY INFERENCE;

EID: 84982793307     PISSN: 03787788     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.enbuild.2016.08.020     Document Type: Article

References (19)

1. [1] Crabtree, G., Solar energy challenges and opportunities. Report of the Basic Energy Sciences Workshop on Solar Energy Utilization, 2013, Materials Science Division, Argonne National Laboratory Available online: engineering.dartmouth.edu/∼d30345d/courses/engs171/energy.pdf, accessed on 29.03.13.
2. [2] Akikur, R., et al. Comparative study of stand-alone and hybrid solar energy systems suitable for off-grid rural electrification: a review. Renew. Sustain. Energy Rev. 27 (2013), 738–752.
3. [3] Chiteka, K., Enweremadu, C.C., Prediction of global horizontal solar irradiance in Zimbabwe using artificial neural networks. J. Clean. Prod. 135:November (1) (2016), 701–711.
4. [4] Yaïci, Wahiba, Entchev, Evgueniy, Adaptive neuro-fuzzy inference system modelling for performance prediction of solar thermal energy system. Renew. Energy 86:February (2016), 302–315.
5. [5] Charabi, Yassine, Gastli, Adel, Al-Yahyai, Sultan, Production of solar radiation bankable datasets from high-resolution solar irradiance derived with dynamical downscaling numerical weather prediction model. Energy Reports 2:November (2016), 67–73.
6. [6] Mazorra Aguiar, L., Pereira, B., Lauret, P., Díaz, F., David, M., Combining solar irradiance measurements, satellite-derived data and a numerical weather prediction model to improve intra-day solar forecasting. Renew. Energy 97:November (2016), 599–610.
7. [7] Aybar-Ruiz, A., Jiménez-Fernández, S., Cornejo-Bueno, L., Casanova-Mateo, C., Sanz-Justo, J., Salvador-González, P., Salcedo-Sanz, S., A novel grouping genetic algorithm–extreme learning machine approach for global solar radiation prediction from numerical weather models inputs. Solar Energy 132:July (2016), 129–142.
8. [8] Yadav, Amit Kumar, Malik, Hasmat, Chandel, S.S., Application of rapid miner in ANN based prediction of solar radiation for assessment of solar energy resource potential of 76 sites in Northwestern India. Renew. Sustain. Energy Rev. 52:December (2015), 1093–1106.
9. [9] Alsina, Emanuel Federico, Bortolini, Marco, Gamberi, Mauro, Regattieri, Alberto, Artificial neural network optimisation for monthly average daily global solar radiation prediction. Energy Convers. Manage. 120:15 (2016), 320–329.
10. [10] Sperati, Simone, Alessandrini, Stefano, Delle Monache, Luca, An application of the ECMWF ensemble prediction system for short-term solar power forecasting. Solar Energy 133:August (2016), 437–450.
11. [11] Kosmopoulos, P.G., Kazadzis, S., Lagouvardos, K., Kotroni Bais, V., Solar energy prediction and verification using operational model forecasts and ground-based solar measurements. Energy 93:December (Part 2) (2015), 1918–1930.
12. [12] Yaïci, Wahiba, Entchev, Evgueniy, Performance prediction of a solar thermal energy system using artificial neural networks. Appl. Therm. Eng. 73:December (1) (2014), 1348–1359.
13. [13] Belaid, S., Mellit, A., Prediction of daily and mean monthly global solar radiation using support vector machine in an arid climate. Energy Convers. Manage. 118:June (15) (2016), 105–118.
14. [14] Sun, Huaiwei, Zhao, Na, Zeng, Xiaofan, Yan, Dong, Study of solar radiation prediction and modeling of relationships between solar radiation and meteorological variables. Energy Convers. Manage. 105:November (15) (2015), 880–890.
15. [15] Jang, J.-S.R., ANFIS: adaptive-network-based fuzzy inference systems. IEEE Trans. Syst. Man Cybern. 23 (1993), 665–685.
16. [16] Petković, D., Issa, M., Pavlović, N.D., Pavlović, N.T., Zentner, L., Adaptive neuro-fuzzy estimation of conductive silicone rubber mechanical properties. Expert Syst. Appl. 39 (2012), 9477–9482 ISSN 0957-4174.
17. [17] Petković, D., Ćojbašić, Ž., Adaptive neuro-fuzzy estimation of automatic nervous system parameters effect on heart rate variability. Neural Comput. Appl. 21:8 (2012), 2065–2070.
18. [18] Kurnaz, S., Cetin, O., Kaynak, O., Adaptive neuro-fuzzy inference system based autonomous flight control of unmanned air vehicles. Expert Syst. Appl. 37 (2010), 1229–1234.
19. [19] Petković, D., Issa, M., Pavlović, N.D., Zentner, L., Ćojbašić, Ž., Adaptive neuro fuzzy controller for adaptive compliant robotic gripper. ISSN 0957-4174 Expert Syst. Appl. 39 (2012), 13295–13304.