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
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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
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)
UNI 10349: Heating and cooling of buildings. Climatic data (1994)
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)
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)
Wong, N.H., Yu, C.: Study of green areas and urban heat island in a tropical city. Habitat International 29(3), 547–558 (2005)
Norgaard, M., Ravn, O., Poulsen, N., Hansen, L.: Neural networks for modelling and control of dynamic systems. Springer, London (2000)
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)
Bagnouls, F., Gaussen, H.: Saison sèche et indice xérothermique. Docum. pour les Cartes des Prod. Végét. Série: Généralité (in French) 1, 1–49 (1953)
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
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)
Mihalakakou, G., Santamoruris, M., Tsangrassoulis, A.: On the energy consumption in residential buildings. Energy and Buildings 34, 727–736 (2002)
Ben-Nakhi, A.E., Mahmoud, M.A.: Cooling load prediction for buildings using general regression neural networks. Energy Conversion & Management 45, 2127–2141 (2004)
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)
American Society of Civil Engineers: Outdoor Human Comfort and Its Assessment: The State of the Art (2004) ISBN-10: 0784406847
Chen, S., Billings, S.A.: Neural Networks for Nonlinear Dynamic System Modelling and Identification. International Journal Control 56(2), 319–346 (1992)
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)
Ljung, L.: System Identification – Theory for the User, 2nd edn. Prentice Hall, Upper Saddle River (1999)
Box, G.E.P., Jenkins, G.M., Reinsel, G.C.: Time Series Analysis, Forecasting and Control, 3rd edn. Prentice Hall, Englewood Clifs (1994)
Norgard, M.: Neural Network Based System Identification TOOLBOX, version 2 (2000), http://www.iau.dtu.dk/research/control/nnsysid.html
Hagan, M.T., Menhaj, M.: Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks 5(6), 989–993 (1994)
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)
Abdel-Aal, R.E.: Hourly temperature forecasting using abductive networks. Engineering Applications of Artificial Intelligence 17, 543–556 (2004)
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)
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)
Galouchko, V.: 3DField (2002), http://field.hypermart.net
Author information
Authors and Affiliations
Editor information
Rights 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)