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2014 | 14 | 4 |

Tytuł artykułu

Short-term forecasting of natural gas demand by rural consumers using regression models

Treść / Zawartość

Warianty tytułu

PL
Prognozowanie krótkookresowe zapotrzebowania odbiorców wiejskich na gaz ziemny z wykorzystaniem modeli regresyjnych

Języki publikacji

EN

Abstrakty

EN
PL

Wydawca

-

Rocznik

Tom

14

Numer

4

Opis fizyczny

p.93-97,fig.,ref.

Twórcy

autor
  • Department of Power Engineering and Agricultural Processes Automation, Agricultural University of Krakow, Balicka Str. 116B, 30-149 Krakow, Poland
  • Department of Power Engineering and Agricultural Processes Automation, Agricultural University of Krakow, Balicka Str. 116B, 30-149 Krakow, Poland

Bibliografia

  • 1. Azadeh A., Asadzadeh S.M., Ghanbari A. 2010: An adaptive network-based fuzzy inference system for shortterm natural gas demand estimation: Uncertain and complex environments. Energy Policy 38, 1529-1536.
  • 2. Azari A.; Shariaty-Niassar M., Alborzi M. 2012: Short-term and medium-term gas demand load forecasting by neural networks. Iranian Journal of Chemistry and Chemical Engineering 31(4), 77-84.
  • 3. Brabec M., Konar O., Pelikan E., Maly M. 2008: A nonlinear mixed effects model for the prediction of natural gas consumption by individual customers. International Journal ofForecasting 24, 659-678.
  • 4. Brabec M., Konar O., Maly M., Pelikan E., Von-dracek J. 2008: A statistical model for natural gas standardized load profiles. JRoy Statist. Soc. Series C: Applied Statistics 58(1), 123-139.
  • 5. Chen Q., She Y., Xu X. 2013: Combination model for short-term load forecasting. The Open Automation and Control Systems Journal 5, 124-132.
  • 6. Dittman P., Szabela-Pasierbińska E. 2007: Short-term sales forecasts in management of natural gas distributing works. Management 11(1), 147-154.
  • 7. Kelner J.M. 2003: Prognozowanie krótkoterminowe poborów gazu z sieci przesyłowych metodą sztucznych sieci neuronowych. Gaz, Woda i Technika Sanitarna 6, 196-204.
  • 8. Kizilaslan R., Karlik B.2008: Comparison neural networks models for short term forecasting of natural gas consumption in Istanbul. In: Applications of digital information and web technologies, ICADIWT 2008, 4-6 August 2008, 448-453.
  • 9. Potocnik P., Govekar E. 2010: Practical results of forecasting for the natural gas market. http://www. intechopen.com/books/natural-gas/practical-re-sults-of-forecasting-for-the natural-gas market.
  • 10. Potocnik P., Govekar E., Grabec I. 2007: Short-term natural gas consumption forecasting. In: Proceedings of the 16th IASTED International Conference on Applied Simulation and Modelling - ASM 2007, 353-357.
  • 11. Potocnik P., Soldo B., Simunovic G., Saric T., Jeromen A., Govekar E. 2014: Comparison of static and adaptive models for short-term residential natural gas forecasting in Croatia. Applied Energy 129, 94-103.
  • 12. Sabo K, ScitovskiR., Vazler I., Zekic-Susac M. 2011: Mathematical models of natural gas consumption. Energy Conversion and Management 52, 1721-1727.
  • 13. Simunek M., Pelikan E. 2008: Temperatures data preprocessing for short-term gas consumption forecast. In: IEEE International Symposium on Industrial Electronics; 1192-1196.
  • 14. Smith P., Husejn S. 1996: Forecasting short term regional gas demand using an expert system. Expert Systems with Applications 10(2), 265-273.
  • 15. Soldo B. 2012: Forecasting natural gas consumption. Applied Energy 92, 26-37.
  • 16. Soldo B., Potocnik P., Simunovic G., Saric T., Govekar E. 2014: Improving the residential natural gas consumption forecasting models by using solar radiation. Energy and Buildings 69, 498-506.
  • 17. Suganthi L., Anand A. Samuelb A.A. 2012: Energy models for demand forecasting-A review. Renewable and Sustainable Energy Reviews 16, 1223-1240.
  • 18. Taspinar F., Celebi N., Tutkun N. 2013: Forecasting of daily natural gas consumption on regional basis in Turkey using various computational methods. Energy and Buildings 56, 23- 31.
  • 19. Yu F., Xu X. 2014: A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network. Applied Energy 134,102-113.
  • 20. Zhou H., Su G., Li G. 2011: Forecasting daily gas load with OIHF-Elman Neural Network. Procedia Computer Science 5, 754-758.

Typ dokumentu

Bibliografia

Identyfikatory

Identyfikator YADDA

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