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 (UHI) phenomenon, known as the increase in the air temperature of urban areas, compared to the one 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 (Italy) has built up a weather monitoring system that works 24 hours per day and makes data available in real-time at the web site: http://www.dream.unipa.it/meteo . The data collected by the system have been used to implement a set of nonlinear black-box models aiming to obtain short-term forecasts of the air temperature and map them over the monitored area. By using the data recorded during the 2008 summer, the daily profiles of the hourly average temperature have been plotted for each weather station of the monitoring system, thus clearly highlighting the temperature differences between the urban and extra-urban area and the average intensity of the UHI of Palermo.
Keywords
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
Abarbanel, H.D.I.: Analysis of Observed Chaotic Data. Springer, New York (1996)
Abdel-Aal, R.E.: Hourly temperature forecasting using abductive networks. Eng. Appl. of Artif. Intell. 17, 543–556 (2004)
Alligood, K., Sauer, T., Yorke, J.A.: Chaos: An Introduction to Dynamical Systems. Springer, New York (1997)
ASCE - American Society of Civil Engineers, Aerodynamics Committee, Outdoor human comfort and its assessment: State of the Art Report. Boston, VA, USA (2004)
Ardente, F., Beccali, G., Cellura, M., Lo Brano, V.: Life cycle assessment of a solar thermal collector: sensitivity analysis, energy and environmental balances. Renew. Energy 30(2), 109–130 (2005)
Beccali, M., Cellura, M., Lo Brano, V., Marvuglia, A.: Forecasting daily urban electric load profiles using artificial neural networks. Energy Convers. and Manag. 45(18/19), 2879–2900 (2004)
Beccali, M., Cellura, M., Lo Brano, V., Marvuglia, A.: Short-term prediction of household electricity consumption: assessing weather sensitivity in a Mediterranean area. Renew. & Sustain. Energy Rev. 12(8), 2040–2065 (2007)
Beccali, G., Cellura, M., Culotta, S., Lo Brano, V., Marvuglia, A.: 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.) ICCSA 2008, Part I. LNCS, vol. 5072, pp. 65–80. Springer, Heidelberg (2008)
Ben-Nakhi, A.E., Mahmoud, M.A.: Cooling load prediction for buildings using general regression neural networks. Energy Convers. & Manag. 45, 2127–2141 (2004)
Chen, S., Billings, S.A.: Neural Networks for Nonlinear Dynamic System Modelling and Identification. Int. J. Control 56(2), 319–346 (1992)
Epstein, Y., Moran, D.S.: Thermal comfort and the heat stress indices. Ind. Health 44, 388–398 (2006)
Gartland, L.: Heat islands: Understanding and Mitigating Heat in Urban Areas. Earthscan Publications, London (2008)
Gautama, T., Mandic, D.P., Van Hulle, M.M.: A differential entropy based method for determining the optimal embedding parameters of a signal. In: Proceedings of ICASSP 2003, Hong Kong, vol. VI, pp. 29–32 (2003)
Gautama, T., Mandic, D.P., Van Hulle, M.M.: The delay vector variance method for detecting determinism and nonlinearity in time series. Physica D 190(3-4), 167–176 (2004)
Gautama, T.: Optimal Embedding Parameters - A differential entropy-based method for determining the optimal embedding parameters of a signal (2007), http://webscripts.softpedia.com/developer/Temu-Gautama-15893.html (accessed October 1, 2008)
González, P., Zamarreño, J.M.: A short-term temperature forecaster based on a state space neural network. Eng. Appl. of Artif. Intell. 15, 459–464 (2002)
Hagan, M.T., Menhaj, M.: Training feedforward networks with the Marquardt algorithm. IEEE Trans. on Neural Netw. 5(6), 989–993 (1994)
Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice-Hall, Englewood Cliffs (1999)
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, vol. 4, pp. 414–419 (2000)
Kantz, H., Schreiber, T.: Nonlinear Time Series Analysis. Cambridge University Press, Cambridge (1997)
Lanza, P.N., Cosme, J.M.: A short-term temperature forecaster based on a novel radial basis functions neural network. Int. J. of Neural Netw. 11, 71–77 (2001)
Ljung, L.: System Identification – Theory for the User, 2nd edn. Prentice Hall, Upper Saddle River (1999)
Ljung, L., Söderström, T.: Theory and Practice of Recursive Identification. MIT Press, Cambridge (1983)
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, vol. II (2001)
Mihalakakou, G., Santamoruris, M., Tsangrassoulis, A.: On the energy consumption in residential buildings. Energy and Build 34, 727–736 (2002)
Norgard, M.: Neural Network Based System Identification TOOLBOX, version 2 (2000), http://www.iau.dtu.dk/research/control/nnsysid.html
Oke, T.R., Johnson, G.T., Steyn, D.G., Watson, I.D.: Simulation of surface urban heat islands under “ideal”conditions at night: part 2. Diagnosis of causation. Bound. Layer Meteorol. 56, 339–358 (1991)
Papadopoulos, A.M.: The influence of street canyons on the cooling loads of buildings and the performance of air conditioning systems. Energy and Build 33, 601–607 (2001)
Santamouris, M., Papanikolaou, N., Livada, I., Koronakis, I., Georgakis, C., Argiriou, A., Assimakopolous, D.N.: On the Impact of Urban Climate on the Energy Consumption of Buildings. Sol. Energy 70(3), 201–216 (2001)
Schreiber, T., Schmitz, A.: Surrogate time series. Physica D 142, 346–382 (2000)
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. Autom. 31(12), 1691–1724 (1995)
Takens, F.: Detecting strange attractors in turbulence. In: Rand, D.A., Young, L.A. (eds.) Dynamical Systems and Turbulence, pp. 366–381. Springer, New York (1981)
Theiler, J., Eubank, S., Longtin, A., Galdrikian, B., Farmer, J.D.: Testing for nonlinearity in time series: the method of surrogate data. Physica D 58(1-4), 77–94 (1992)
UNI 10349 Heating and cooling of buildings. Climatic data (1994)
Wong, N.H., Yu, C.: Study of green areas and urban heat island in a tropical city. Habitat Int. 29(3), 547–558 (2005)
Yang, I.H., Kim, W.K.: Prediction of the time of room air temperature descending for heating systems in buildings. Build. and Environ. 39, 19–29 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Cellura, M., Culotta, S., Brano, V.L., Marvuglia, A. (2011). Nonlinear Black-Box Models for Short-Term Forecasting of Air Temperature in the Town of Palermo. In: Murgante, B., Borruso, G., Lapucci, A. (eds) Geocomputation, Sustainability and Environmental Planning. Studies in Computational Intelligence, vol 348. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19733-8_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-19733-8_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19732-1
Online ISBN: 978-3-642-19733-8
eBook Packages: EngineeringEngineering (R0)