Department of Power Engineering and Agricultural Processes Automation, Agricultural University of Krakow, Balicka Str. 116B, 30-149 Krakow, Poland
Bibliografia
1. Analiza i prognoza obciążeń elektroenergetycznych. Praca zbiorowa 1971: WN-T Warszawa.
2. 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.
3. Bianco V., Scarpa F., Tagliafico L.A. 2014: Analysis and future outlook of natural gas consumption in the Italian residential sector. Energy Conversion and Management 87, 754-764.
4. 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.
5. Brabec M., Konar O., Maly M., Pelikan E., Vondracek J. 2008: A statistical model for natural gas standardized load profiles. JRoy Statist. Soc. Series C: Applied Statistics 58(1), 123-39.
6. Chen Q., She Y., Xu X. 2013: Combination model for short-term load forecasting. The Open Automation and Control Systems Journal 5, 124-132.
7. Chicco G. 2012: Overview and performance assessment of the clustering methods for electrical load pattern grouping. Energy 42, 68-80.
8. Ferreira A.M.S., Cavalcante C.A., Fontes C.H., Marambio J. 2012: Anew proposal of typification of load profiles to support the decision-making in the sector of electric energy distribution. Internacional Industrial Conference on Engineering and Operations Management 9-12 July 2012 Guimaraes Portugalia, ID 18.1-ID18.7.
9. Dudek G. 2004: Wybrane metody analizy szeregów czasowych obciążeń elektroenergetycznych. Materiały Konferencji Naukowej Prognozowanie w elektroenergetyce PE ,04. Częstochowa, 116 - 125.
10. Góra S. 1975: Gospodarka elektroenergetyczna w przemyśle. PWN Warszawa.
11. Kelner J.M. 2003: Prognozowanie krótkoterminowe poborów gazu z sieci przesyłowych metodą sztucznych sieci neuronowych. Gaz, Woda i Technika Sanitarna 6,196-204.
12. Melikoglu M. 2013: Vision 2023: Forecasting Turkey’s natural gas demand between 2013 and 2030. Renewable and Sustainable Energy Reviews 22, 393-400.
13. Nai-ming X., Chao-qing Y., Ying-jie Y. 2015: Forecasting China’s energy demand and self-sufficiency rate by grey forecasting model and Markov model. Electrical Power and Energy Systems 66, 1-8.
14. Potocnik P., Soldo B., Simunovic G., Śarić 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.
15. Prognozowanie w elektroenergetyce. Zagadnienia wybrane (red. I. Dobrzańska). 2002: Wydawnictwo Politechniki Częstochowskiej, Częstochowa.
16. Smith P., Husejn S. 1997: Forecasting short term regional gas demand using an expert system. Expert Systems with Applications 10(2), 265-273.
17. Soldo B. 2012: Forecasting natural gas consumption. Applied Energy 92, 26-37.
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. Vondracek, J.; Pelikan, E.; Konar O.; Cermakova J.; Eben K.; Maly M., Brabec M. 2008: A statistical model for the estimation of natural gas consumption. Applied Energy 85, 362-370.
20. Yu F., Xul 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.