Natural Hazards

, Volume 73, Issue 2, pp 373–387 | Cite as

Cold and warm air temperature spells during the winter and summer seasons and their impact on energy consumption in urban areas

  • Stevan Savić
  • Aleksandar Selakov
  • Dragan Milošević
Original Paper


The main objective of this study is to determine and analyze cold and warm air temperature spells in the last 6 years (2007–2012) and reveal their impact on electrical energy consumption in a small-sized city such as Sombor (Serbia) with less than 50,000 inhabitants. Hourly air temperature values and electrical energy consumption data have been used as database for all methods. Warm and cold temperature spells (during heat and cold waves) have had the increasing tendencies in the last 6 years and they reflect on additional electrical energy consumption. Detailed analysis showed that higher energy demands occur during workdays and daytime period. Monitoring of the amount of consumed energy showed a clear relationship during the winter cold temperature spells, when electrical energy demand was higher than 0.3 MW. In summer period, the relationship was weaker and consumption was higher than 0.15 MW only when temperature exceeded 30 °C. A small number of air condition devices in houses and companies and mainly one-store buildings with thick walls, which make a good insulation from the outside air temperatures, are probably the main reasons for the above-mentioned results in summer. This paper introduces a new method to resolve the problem of short-term load forecasting, based on the support vector machines (SVM) technology and particle swarm optimization that has been used to optimize the SVM parameters. Similar-day-based forecast has shown that similar days for training should be filtered also using classifier of temperature period (cooling degree-days or heating degree-days in a row). Forecasting error is smaller compared to solutions where similar days are found only on season and temperature.


Air temperature Energy consumption Cold waves Heat waves Forecasting analysis Serbia 



Authors would like to give thanks to Republic Hydrometeorological Service of Serbia for providing hourly air temperature data from meteorological station in Sombor. This research is partly supported by the Project No. 43002, financed by the Ministry of Education, Science and Technological Development of the Republic of Serbia.


  1. Blazejczyk K, Bakowska M, Wieclaw M (2006) Urban heat island in large and small cities. 6th International Conference on Urban Climate, June 12–16, Goteborg, Sweden. pp 794–797Google Scholar
  2. Bolatturk A (2008) Optimum insulation thickness for building walls with respect to cooling and heating degree-hours in the warmest zone of Turkey. Build Environ 43:1055–1064CrossRefGoogle Scholar
  3. Burgin DG, Crespo JA, Hartman A, Hendricks AD, Heuss J, Hudson KA, Knutson R, Pearson A, Moser D, Trapp RJ (2013) Observations of the urban heat island effect from a small city. 93th American meteorological society annual meeting, January 5–10, Austin, USAGoogle Scholar
  4. Chen Y, Luh PB, Guan C, Zhao Y, Michel LD, Coolbeth MA, Friedland PB, Rourke SJ (2010) Short-term load forecasting: similar day-based wavelet neural networks. IEEE T Power Syst 25:322–330CrossRefGoogle Scholar
  5. Cheval S, Dumitrescu A, Bell A (2009) The urban heat island of Bucharest during the extreme high temperatures of July 2007. Theor Appl Climatol 97:391–401CrossRefGoogle Scholar
  6. de Lucena AJ, Filho OCR, de Almeida França JR, de Faria PL, Xavier LNR (2013) Urban climate and clues of heat island events in the metropolitan area of Rio de Janeiro. Theor Appl Climatol 111:497–511CrossRefGoogle Scholar
  7. Franck U, Krüger M, Schwarz N, Grossmann K, Röder S, Schlink U (2013) Heat stress in urban areas: indoor and outdoor temperatures in different urban structure types and subjectively reported well-being during a heat wave in the city of Leipzig. Meteorol Z 22:167–177CrossRefGoogle Scholar
  8. Gál T, Unger J (2009) Detection of ventilation paths using high-resolution roughness parameter mapping in a large urban area. Build Environ 44:198–206CrossRefGoogle Scholar
  9. Kennedy J, Eberhart R (1995) Particle Swarm Optimization. In: IEEE international conference on neural network, Perth, Australia 1942–1948Google Scholar
  10. Kolokotroni M, Giannitrasis J, Watkins R (2006) The effect of the London urban heat island on building summer cooling demand and night ventilation strategies. Sol Energy 80:383–392CrossRefGoogle Scholar
  11. Kottek M, Grieser J, Beck C, Rudolf B, Rubel F (2006) World map of the Köppen–Geiger climate classification updated. Meteorol Z 15:259–263CrossRefGoogle Scholar
  12. Kyselỳ J (2010) Recent severe heat waves in central Europe: how to view them in a long-term prospect? Int J Climatol 30:89–109Google Scholar
  13. Lee VW (2013) Historical global analysis of occurrences and human casualty of extreme temperature events (ETEs). Nat Hazards. doi: 10.1007/s11069-013-0884-7
  14. Liu W, You H, Dou J (2009) Urban-rural humidity and temperature differences in the Beijing area. Theor Appl Climatol 96:201–207CrossRefGoogle Scholar
  15. Mandal P, Senjyu T, Urasaki N, Funabashi T (2006) A neural network based several-hour-ahead electric load forecasting using similar days approach. Int J Electr Power 28:367–373CrossRefGoogle Scholar
  16. Manly G (1958) On the frequency of snowfall in metropolitan England. Q J R Meteor Soc 84:70–72CrossRefGoogle Scholar
  17. Municipality Administration of Sombor (MAS) (2006) Directive 352-858/2006-V, pp 23 (in Serbian)Google Scholar
  18. Novi Sad City Government (NSCG) (2013) Directive 352-1226/2013-I, pp 72 (in Serbian)Google Scholar
  19. Oke TR (2006) Towards better scientific communication in urban climate. Theor Appl Climatol 84:179–190CrossRefGoogle Scholar
  20. Oke TR (2009) The need to establish protocols in urban heat island work. Symposium & Eighth Symposium on Urban Environment, 11–15 Jan, Phoenix.
  21. Oke TR, Maxwell GB (1975) Urban heat island dynamics in Montreal and Vancouver. Atmos Environ 9:191–200CrossRefGoogle Scholar
  22. Park DC, El-Sharkawi MA, Marks RJ II, Atlas LE, Damborg MJ (1991) Electric load forecasting using an artificial neural network. IEEE Trans Power Eng 6:442–449CrossRefGoogle Scholar
  23. Population Reference Bureau (2010).
  24. RHMZ (2008) Basic climate characteristics in Serbia in the period January–December 2007. Republic Hydrometeorogical Service, National Centre for climate change, Department of climate forecast, information and instruction, Belgrade. (in Serbian)
  25. RHMZ (2012) Extreme climate events analysis in Serbia: cold wave in February 2012. Republic Hydrometeorogical Service, National Centre for climate change, Department of climate forecast, information and instruction, Belgrade. (in Serbian)
  26. RHMZ (2013) Importance of climate events in Serbia territory during 2012. Republic Hydrometeorogical Service, National Centre for climate change, Department of climate forecast, information and instruction, Belgrade. (in Serbian)
  27. Santoamouris M, Papanikolaou N, Livada I, Koronakis I, Georgakis C, Argiriou A, Assimakopoulos DN (2001) On the impact of urban climate on the energy consumption of buildings. Sol Energy 70:201–216CrossRefGoogle Scholar
  28. Selakov A, Ilić S, Vukmirović S, Kulić F, Erdeljan A, Gorečan Z (2012) A comparative analysis of SVM and ANN based hybrid model for short term load forecasting. Transmission and distribution conference and exposition (T&D), 2012 IEEE PES: 1–5Google Scholar
  29. Statistical Office of the Republic of Serbia (2012) Population in 2011 census of population, households and dwellings in the Republic of Serbia, Belgrade: 101Google Scholar
  30. Stewart ID (2007) Landscape representation and the urban-rural dichotomy in empirical urban heat island literature, 1950–2006. Acta Climatologica et Chorologica 40–41:111–121Google Scholar
  31. Stewart ID (2011) A systematic review and scientific critique of methodology in modern urban heat island literature. Int J Climatol 31:200–217CrossRefGoogle Scholar
  32. Twardosz R, Kossowska-Cezak U (2013) Exceptionally hot summers in Central and Eastern Europe (1951–2010). Theor Appl Climatol 112:617–628CrossRefGoogle Scholar
  33. Unger J, Makra L (2007) Urban-rural difference in the heating demand as a consequence of the heat island. Acta Climatologica et Chorologica 40–41:155–162Google Scholar
  34. Unger J, Sümeghy Z, Szegedi S, Kiss A, Géczi R (2010) Comparison and generalisation of spatial patterns of the urban heat island based on normalized values. Phys Chem Earth 35:107–114CrossRefGoogle Scholar
  35. Unger J, Savić S, Gál T (2011) Modelling of the annual mean urban heat island pattern for planning of representative urban climate station network. Adv Meteorol 2011:9. doi: 10.1155/2011/398613 CrossRefGoogle Scholar
  36. Unkašević M, Tošić I (2009) An analysis of heat waves in Serbia. Global Planet Change 65:17–26CrossRefGoogle Scholar
  37. Vapnik V (1995) The nature of statistical learning theory. Springer, New YorkCrossRefGoogle Scholar
  38. Vapnik V (1998) Statistical learning theory. Wiley, New YorkGoogle Scholar
  39. Vardoulakis E, Karamanis D, Fotiadi A, Mihalakakou G (2013) The urban heat island effect in a small Mediterranean city of high summer temperatures and cooling energy demands. Sol Energy 94:128–144CrossRefGoogle Scholar
  40. Vortice (2006) Technical guide, pp 22 (in Serbian)Google Scholar
  41. Wai Siu L, Hart MA (2013) Quantifying urban heat island intensity in Hong Kong SAR, China. Environ Monit Assess 185:4383–4398CrossRefGoogle Scholar
  42. Wang L, Singh C (2007) PSO-based hybrid generating system design incorporating reliability evaluation and generation/load forecasting. Power tech conference, IEEE PES, 1–5 July, Lausanne, Switzerland: 1392–1397Google Scholar
  43. Wua CH, Tzeng GH, Lin RH (2009) A novel hybrid genetic algorithm for kernel function and parameter optimization in support vector regression. Expert Syst Appl 36:4725–4735CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Stevan Savić
    • 1
  • Aleksandar Selakov
    • 2
  • Dragan Milošević
    • 1
  1. 1.Faculty of Science, Climatology and Hydrology Research CentreUniversity of Novi SadNovi SadSerbia
  2. 2.Schneider Electric DMS NSNovi SadSerbia

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