Development of a model for urban heat island prediction using neural network techniques

Abstract The urban heat island (UHI) phenomenon is mainly caused by the differences in the thermal behaviour between urban and rural settlements that are associated with the thermal properties of urban materials, urban geometry, air pollution, and the anthropogenic heat released by the urban activities. The UHI has a serious impact on the energy consumption of buildings, increases smog production, while contributing to an increasing emission of pollutants from power plants, including sulfur dioxide, carbon monoxide, nitrous oxides and suspended particulates. This study presents the applicability of artificial neural networks (ANNs) and learning paradigms for UHI intensity prediction in Athens, Greece. The proposed model is tested using Elman, Feed-Forward and Cascade neural network architecture. The data of time, ambient temperature and global solar radiation are used to train and test the different models. The prediction accuracy is analyzed and evaluated.

[1]  Aris Tsangrassoulis,et al.  Simulation of the Urban Heat Island Phenomenon in Mediterranean Climates , 2004 .

[2]  K. Pavlou,et al.  Recent progress on passive cooling techniques: Advanced technological developments to improve survivability levels in low-income households , 2007 .

[3]  Aris Tsangrassoulis,et al.  Modifications in energy demand in urban areas as a result of climate changes: an assessment for the southeast Mediterranean region , 2001 .

[4]  G. Mihalakakou,et al.  Determination of places in the great Athens area where the heat island effect is observed , 2002 .

[5]  H. Akbari,et al.  Cooling energy savings potential of reflective roofs for residential and commercial buildings in the United States , 1999 .

[6]  D. Asimakopoulos.  Energy and Climate in the Urban Built Environment , 2001 .

[7]  M. Santamouris,et al.  Heat Island Research in Europe: The State of the Art , 2007 .

[8]  Maria Kolokotroni,et al.  A validated methodology for the prediction of heating and cooling energy demand for buildings within the Urban Heat Island: Case-study of London , 2010 .

[9]  V. Prybutok,et al.  A neural network model forecasting for prediction of daily maximum ozone concentration in an industrialized urban area. , 1996, Environmental pollution.

[10]  N. Klitsikas,et al.  The effect of the Athens heat island on air conditioning load , 2000 .

[11]  Aris Tsangrassoulis,et al.  On the cooling potential of night ventilation techniques in the urban environment , 2005 .

[12]  Yun Zeng,et al.  Progress in developing an ANN model for air pollution index forecast , 2004 .

[13]  M. N. Assimakopoulos,et al.  On the variability of summer air temperature during the last 28 years in Athens , 2007 .

[14]  M. Santamouris,et al.  Estimating the ecological footprint of the heat island effect over Athens, Greece , 2007 .

[15]  F. Haghighat,et al.  Approaches to study Urban Heat Island – Abilities and limitations , 2010 .

[16]  Helena A. Flocas,et al.  Application of Neural Networks to the Simulation of the Heat Island over Athens, Greece, Using Synoptic Types as a Predictor , 2002 .

[17]  Maria Kolokotroni,et al.  THE EFFECT OF THE LONDON URBAN HEAT ISLAND ON BUILDING SUMMER COOLING DEMAND AND NIGHT VENTILATION STRATEGIES , 2006 .

[18]  Paul J. Crutzen,et al.  New Directions: The growing urban heat and pollution "island" effect - impact on chemistry and climate , 2004 .

[19]  Kamran Sepanloo,et al.  Estimation of research reactor core parameters using cascade feed forward artificial neural networks , 2009 .

[20]  Shafiqur Rehman,et al.  Application of neural networks for the prediction of hourly mean surface temperatures in Saudi Arabia , 2002 .

[21]  António E. Ruano,et al.  Prediction of building's temperature using neural networks models , 2006 .

[22]  D. N. Asimakopoulos,et al.  A neural network approach for modeling the Heat Island phenomenon in urban areas during the summer period , 1999 .

[23]  Ajith Abraham,et al.  An ensemble of neural networks for weather forecasting , 2004, Neural Computing & Applications.

[24]  Qing Song,et al.  On the Weight Convergence of Elman Networks , 2010, IEEE Transactions on Neural Networks.