Skip to main content
Springer Nature Link
Log in

A Web-Based Autonomous Weather Monitoring System of the Town of Palermo and Its Utilization for Temperature Nowcasting

  • Conference paper
Computational Science and Its Applications – ICCSA 2008 (ICCSA 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5072))

Included in the following conference series:

  • 3322 Accesses

Abstract

Weather data are crucial to correctly design buildings and their heating and cooling systems and to assess their energy performances. In the intensely urbanized towns the effect of climatic parameters is further emphasized by the “urban heat island” phenomenon, known as the increase in the air temperature of urban areas, compared to the conditions measured in the extra-urban areas. The analysis of the heat island needs detailed local climate data which can be collected only by a dedicated weather monitoring system. The Department of Energy and Environmental Researches of the University of Palermo has built up a weather monitoring system that works 24 hours per day and makes data available in real-time at the web site: www.dream.unipa.it/meteo. The data collected by the system have been used to implement a NNARMAX model aiming to obtain short-term forecasts of the temperature and map them over the monitored area.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Beccali, M., Cellura, M., Lo Brano, V., Marvuglia, A.: Short-term prediction of household electricity consumption: assessing weather sensitivity in a Mediterranean area. Renewable & Sustainable Energy Reviews (2007) ISSN: 1364-0321. doi:10.1016/j.rser.2007.04.010

    Google Scholar 

  2. Ardente, F., Beccali, G., Cellura, M., Lo Brano, V.: Life cycle assessment of a solar thermal collector: sensitivity analysis, energy and environmental balances. Renewable Energy 30(2), 109–130 (2005)

    Article  Google Scholar 

  3. UNI 10349: Heating and cooling of buildings. Climatic data (1994)

    Google Scholar 

  4. Lopes, C., Adnot, J., Santamouris, M., Klitsikas, N., Alvarez, S., Sanchez, F.: Managing the Growth of the Demand for Cooling in Urban Areas and Mitigating the Urban Heat Island Effect. In: European Council for an Energy Efficient Economy (ECEEE) congress, Mandelieu, June 11-16, 2001, vol. II (2001)

    Google Scholar 

  5. Kolokotroni, M., Giannitsaris, I., Watkins, R.: The effect of the London urban heat island on building summer cooling demand and night ventilation strategies. Solar Energy 80(4), 383–392 (2006)

    Article  Google Scholar 

  6. Wong, N.H., Yu, C.: Study of green areas and urban heat island in a tropical city. Habitat International 29(3), 547–558 (2005)

    Article  Google Scholar 

  7. Norgaard, M., Ravn, O., Poulsen, N., Hansen, L.: Neural networks for modelling and control of dynamic systems. Springer, London (2000)

    Google Scholar 

  8. World Meteorological Organization: 1961–1990 global climate normals (CLINO). CD-ROM version 1.0, November 1998. Produced by National Climatic Data, Center, NOAA, USA (1998)

    Google Scholar 

  9. Bagnouls, F., Gaussen, H.: Saison sèche et indice xérothermique. Docum. pour les Cartes des Prod. Végét. Série: Généralité (in French) 1, 1–49 (1953)

    Google Scholar 

  10. Micela, G., Granata, L., Iuliano, V.: Due secoli di pioggia a Palermo; Report of the Astronomic Observatory of Palermo G.S. Vaiana, University of Palermo (in Italian) (2001) ISBN 99-87905-2-09

    Google Scholar 

  11. Sözen, A., Arcaklıoglu, E., Özalp, M., Kanıt, E.G.: Use of artificial neural networks for mapping of solar potential in Turkey. Applied Energy 77, 273–286 (2004)

    Article  Google Scholar 

  12. Mihalakakou, G., Santamoruris, M., Tsangrassoulis, A.: On the energy consumption in residential buildings. Energy and Buildings 34, 727–736 (2002)

    Article  Google Scholar 

  13. Ben-Nakhi, A.E., Mahmoud, M.A.: Cooling load prediction for buildings using general regression neural networks. Energy Conversion & Management 45, 2127–2141 (2004)

    Article  Google Scholar 

  14. Yang, I.H., Kim, W.K.: Prediction of the time of room air temperature descending for heating systems in buildings. Building and Environment 39, 19–29 (2004)

    Article  Google Scholar 

  15. American Society of Civil Engineers: Outdoor Human Comfort and Its Assessment: The State of the Art (2004) ISBN-10: 0784406847

    Google Scholar 

  16. Chen, S., Billings, S.A.: Neural Networks for Nonlinear Dynamic System Modelling and Identification. International Journal Control 56(2), 319–346 (1992)

    Article  MATH  MathSciNet  Google Scholar 

  17. Sjöberg, J., Zhang, Q., Ljung, L., Benveniste, A., Delyon, B., Glorennec, P., Hjalmarsson, H., Juditsky, A.: Nonlinear black-box modeling in system identification: a unified overview. Automatica 31(12), 1691–1724 (1995)

    Article  MATH  MathSciNet  Google Scholar 

  18. Ljung, L.: System Identification – Theory for the User, 2nd edn. Prentice Hall, Upper Saddle River (1999)

    Google Scholar 

  19. Box, G.E.P., Jenkins, G.M., Reinsel, G.C.: Time Series Analysis, Forecasting and Control, 3rd edn. Prentice Hall, Englewood Clifs (1994)

    MATH  Google Scholar 

  20. Norgard, M.: Neural Network Based System Identification TOOLBOX, version 2 (2000), http://www.iau.dtu.dk/research/control/nnsysid.html

  21. Hagan, M.T., Menhaj, M.: Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks 5(6), 989–993 (1994)

    Article  Google Scholar 

  22. González, P., Zamarreño, J.M.: A short-term temperature forecaster based on a state space neural network. Engineering Applications of Artificial Intelligence 15, 459–464 (2002)

    Article  Google Scholar 

  23. Abdel-Aal, R.E.: Hourly temperature forecasting using abductive networks. Engineering Applications of Artificial Intelligence 17, 543–556 (2004)

    Article  Google Scholar 

  24. Lanza, P.N., Cosme, J.M.: A short-term temperature forecaster based on a novel radial basis functions neural network. International Journal of Neural Networks 11, 71–77 (2001)

    Google Scholar 

  25. Hippert, H.S., Pedreira, C.E., Souza, R.C.: Combining neural networks and ARIMA models for hourly temperature forecast. In: IEEE-INNS-ENNS International Joint Conference on Neural Networks, Como (Italy), July 24-27, 2000, vol. 4, pp. 414–419 (2000)

    Google Scholar 

  26. Galouchko, V.: 3DField (2002), http://field.hypermart.net

Download references

Author information

Authors and Affiliations

  1. Dipartimento di Ricerche Energetiche ed Ambientali (DREAM), Università degli Studi di Palermo, viale delle scienze, edificio 9, 90128, Palermo, Italy

    Giorgio Beccali, Maurizio Cellura, Simona Culotta, Valerio Lo Brano & Antonino Marvuglia

Authors
  1. Giorgio Beccali
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maurizio Cellura
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Simona Culotta
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Valerio Lo Brano
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Antonino Marvuglia
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Osvaldo Gervasi Beniamino Murgante Antonio Laganà David Taniar Youngsong Mun Marina L. Gavrilova

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Beccali, G., Cellura, M., Culotta, S., Lo Brano, V., Marvuglia, A. (2008). A Web-Based Autonomous Weather Monitoring System of the Town of Palermo and Its Utilization for Temperature Nowcasting. In: Gervasi, O., Murgante, B., Laganà, A., Taniar, D., Mun, Y., Gavrilova, M.L. (eds) Computational Science and Its Applications – ICCSA 2008. ICCSA 2008. Lecture Notes in Computer Science, vol 5072. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69839-5_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-69839-5_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69838-8

  • Online ISBN: 978-3-540-69839-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics