Generation of ambient temperature hourly time series for some Spanish locations by artificial neural networks

Renewable Energy

Volumn 51, Issue , 2013, Pages 285-291

  Almonacid, F ^a   Perez Higueras P ^a   Rodrigo, P ^a   Hontoria, L ^a  

^a UNIVERSITY OF JAÉN   (Spain)

Author keywords

Ambient temperature hourly time series; Artificial neural network

Indexed keywords

TRAINING AND TESTING;

TEMPERATURE; TIME SERIES;

NEURAL NETWORKS;

AIR TEMPERATURE; AMBIENT AIR; ARTIFICIAL NEURAL NETWORK; TEMPERATURE PROFILE; TIME SERIES; TREND ANALYSIS;

SPAIN;

EID: 84867754607     PISSN: 09601481     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.renene.2012.09.022     Document Type: Article

References (34)

1. Cuomo V., Fontana F., Serio C. Behaviour of ambient temperature on daily basis in Italian climate. Rev Phys Appl 1986, 21:211-218.
2. Amato U., Cuomo V., Fontana F., Serio F.C. Statistical predictability and parametric models of daily ambient temperature and solar irradiance: an analysis in the Italian climate. J Appl Meteor 1989, 28:711-721.
3. Hernandez E., Garcia R., Teso M.T. Minimum temperature forecasting by stochastic techniques: an evidence of the heat island effect. Mausam 1991, 41:161-166.
4. Macchiato M., Serio C., Lapenna V., La Rotonda L. Parametric time series analysis of cold and hot spells in daily temperature: an application in southern Italy. J Appl Meteor 1993, 32:1270-1281.
5. Aguiar R. Series sintéticas de parámetros meteorológicos (Synthetic series of meterological parameters). Ph.D. thesis. Lisbon University, Lisbon, Portugal; 1996. p. 525.
6. Erbs D.G., Klein S.A., Beckman W.A. Estimation of degree day and ambient temperature bin data from monthly-average temperatures. ASHRAE J 1983, 25:60-65.
7. Knight K.M., Klein S.A., Duffie J.A. A methodology for the synthesis of hourly weather data. Sol Energy 1991, 46:109-120.
8. Heinemann D, Langer C, Schumacher J. Synthesis of hourly ambient temperature time series correlated with solar radiation. Proc. EuroSun'96 Conf., Freiburg, Germany, ISES-Europe. [Available from: Energy Department, Oldenburg University, D-26111 Oldenburg, Germany]; 1996. p. 1518-23.
9. Huld A.T., Suri M., Dunlop Ewan D., Micaele F. Estimating average daytime and daily temperature profiles within Europe. Environ Model Software 2006, 21:1650-1661.
10. Khotanzad A., Davis M.H., Abaye A. An artificial neural network hourly temperature forecaster with applications in load forecasting. IEEE Trans Power Syst 1996, 11(2):870-876.
11. Bilbao J., deMiguel A., Kambezidis H.D. Air temperature model evaluation in the north Mediterranean belt area. J Appl Meteor 2001, 41:872-884.
12. Tasadduq I., Rehman S., Bubshait K. Application of neural networks for the prediction of hourly mean surface temperatures in Saudi Arabia. Renew Energy 2002, 25:545-554.
13. Hippert H.S., Pedreira C.E. Estimating temperature profiles for short-term load forecasting: neuralnetworks compared to linear models. IEE Proc Gener Transm Distrib 2004, 151(4).
14. Hippert H.S., Pedreira C.E., Souza R.C. Combining neural networks and ARIMA models for hourly temperature forecast. IEEE-INNS-ENNS international joint conference on neural networks (IJCNN'00) 2000, vol. 4.
15. Ceravolo F., Di Pietra B., Pizzuti S., Puglisi G. Neural models for ambient temperature modeling. IEEE international conference on computational intelligence for measurement systems and applications Istanbul-Turkey July 2008, 14-16.
16. Altan O., Çivril Ö. Estimation of hourly mean ambient temperatures with artificial neural networks. Math Comput Appl 2006, 11(3):215-224.
17. Adelard L, Thierry M, Boyer H, Gatina JC. Elaboration of a new tool for weather data sequences generation.
18. Morabito F.C., Versaci M. Fuzzy neural identification and forecasting techniques to process experimental urban air pollution data. Neural Networks April-May 2003, 16(3-4):493-506.
19. Marra S., Morabito F.C., Versaci M. Neural networks and Cao's method: a novel approach for air pollutants time series forecasting. Proceedings of the international joint conference on neural networks, 2003;Portland, OR 2003, vol. 4:2448-2453.
20. Hush D.R., Horne B.G. Progress in supervised neural networks. What's new since Lippmann 1993, IEEE, S.P. Magazine (1993) p. 8-39.
21. Kohonen T. Self-organising maps 1995, Verlag, Springer, Berlin.
22. Cybenko G. Approximation by superpositions of a sigmoidal function. Math Contr Signals Syst 1989, 2:303-314.
23. Funahashi K. On the approximate realisation of continuous mappings by neural networks. Neural Networks 1989, 2:183-192.
24. Hornik K., Stinchcombe M., White H. Multilayer feedforward networks are universal approximators. Neural Networks 1989, 2(5):359-366.
25. Rumelhart D., MacClelland J.L. Learning internal representations by error backpropagation. Chapter 8 from parallel distributed processing. Foundations 1986, vol. 1. The MIT Press.
26. Werbos P. Beyond regression: new tools for prediction and analysis in the behavioural sciences. Ph.D. dissertation, Committee on Appl. Math., Harvard Univ., Cambridge, MA; 1974.
27. Reed R. Pruning algorithms-a survey. IEEE Trans Neural Networks 1993, 4(5):740-747.
28. Ghosh A.K., Lubkeman D.L. The classification of power system disturbance waveforms using a neural network approach. IEEE Trans Power Deliv 1995, 10(1):109-115.
29. Zurada J.M. Introduction to artificial neural systems 1992, West Publishing Company.
30. Curry B., Morgan P.H. Model selection in neural networks: some difficulties. Eur J Operat Res 2004.
31. Macchiato M., La Rotonda L., Lapenna V., Ragosta M. Time modeling and spatial clustering of daily ambient temperature: an application in southern Italy. Environmetrics 1995, 6:31-53.
32. AEMET/agencia Estatal de METeorología: http://www.aemet.com/.
33. Moller M.F. A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks 1993, 6:525-533.
34. de Miguel A., Bilbao J., Aguiar R., Kambezidis H., Negro E. Diffuse solar irradiation model evaluation in the north Mediterranean belt area. Sol Energy 2001, 70:143-153.