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Nonlinear Black-Box Models for Short-Term Forecasting of Air Temperature in the Town of Palermo

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Geocomputation, Sustainability and Environmental Planning

Part of the book series: Studies in Computational Intelligence ((SCI,volume 348))

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.

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References

  • Abarbanel, H.D.I.: Analysis of Observed Chaotic Data. Springer, New York (1996)

    MATH  Google Scholar 

  • Abdel-Aal, R.E.: Hourly temperature forecasting using abductive networks. Eng. Appl. of Artif. Intell. 17, 543–556 (2004)

    Article  Google Scholar 

  • Alligood, K., Sauer, T., Yorke, J.A.: Chaos: An Introduction to Dynamical Systems. Springer, New York (1997)

    Google Scholar 

  • ASCE - American Society of Civil Engineers, Aerodynamics Committee, Outdoor human comfort and its assessment: State of the Art Report. Boston, VA, USA (2004)

    Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Chapter  Google Scholar 

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

    Article  Google Scholar 

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

    Article  MATH  MathSciNet  Google Scholar 

  • Epstein, Y., Moran, D.S.: Thermal comfort and the heat stress indices. Ind. Health 44, 388–398 (2006)

    Article  Google Scholar 

  • Gartland, L.: Heat islands: Understanding and Mitigating Heat in Urban Areas. Earthscan Publications, London (2008)

    Google Scholar 

  • 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)

    Google Scholar 

  • 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)

    Article  MATH  MathSciNet  Google Scholar 

  • 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)

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice-Hall, Englewood Cliffs (1999)

    MATH  Google Scholar 

  • 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)

    Google Scholar 

  • Kantz, H., Schreiber, T.: Nonlinear Time Series Analysis. Cambridge University Press, Cambridge (1997)

    MATH  Google Scholar 

  • 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)

    Google Scholar 

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

    Google Scholar 

  • Ljung, L., Söderström, T.: Theory and Practice of Recursive Identification. MIT Press, Cambridge (1983)

    MATH  Google Scholar 

  • 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)

    Google Scholar 

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

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Schreiber, T., Schmitz, A.: Surrogate time series. Physica D 142, 346–382 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  • 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)

    Article  MATH  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • 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)

    Article  MATH  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

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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

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  • 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

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