Open Access
Issue
E3S Web Conf.
Volume 137, 2019
XIV Research & Development in Power Engineering (RDPE 2019)
Article Number 01006
Number of page(s) 6
DOI https://doi.org/10.1051/e3sconf/201913701006
Published online 16 December 2019
  1. Lackie W., The Influence of Load and Diversity Factors on Methods of Charging for Electrical Energy, Journal of the Institution of Electrical Engineers, vol. 42, pp. 100–114, (1909). [CrossRef] [Google Scholar]
  2. Gear H., Diversity factor, the Chicago Section of the American Institute of Electrical Engineers, (1910). [Google Scholar]
  3. Bary C., Coincidence-Factor Relationships of Electric-Service-Load Characteristics, AIEE Transactions, vol. 64, pp. 623–629, (1945). [Google Scholar]
  4. Sargent A., Broadwater, R., Thompson J., Nazarko J., Estimation of Diversity and kWh-to-Peak- kW Factors from Load Research Data, IEEE Trans. on Power Systems, vol. 9, pp. 1450–1456, (1994). [CrossRef] [Google Scholar]
  5. Nazarko J., Broadwater R., Tawalbeh N., Identification of Statistical Properties of Diversity and Conversion Factors from Load Research Data, Electrotechnical Conference, vol. 1, (1998). [Google Scholar]
  6. K. Billewicz,, Tests normally distributed population of the values energy (in Polish), Przeglqd Elektrotechniczny, no. 4, pp. 78–79, (2008). [Google Scholar]
  7. Dickert J., Schegner P., Residential Load Models for Network Planning Purposes, IEEE 2010 Modern Electric Power Systems, Wroclaw, (2010). [Google Scholar]
  8. Peppanen J., Taylor J., Enhanced Load Modeling with Expanded System Monitoring, IEEE Clemson University Power Systems Conference (PSC), (2018). [Google Scholar]
  9. Liu X., Zheng H., Song S., Fu G., Selection Method of Community Load Coincidence Factor Based on BP Neural Network, IEEE 3rd International Conference on Computer Science and Network Technology, (2013). [Google Scholar]
  10. McQueen D.H., Hyland P., Watson S., Monte Carlo Simulation of Residential Electricity Demand for Forecasting Maximum Demand on Distribution Networks, IEEE Trans. on Power Systems, vol. 19, no.3, pp .1685–1689, (2004). [CrossRef] [Google Scholar]
  11. Chatlani D.H., Tylavsky P., Montgomery S., Dyer S., Statistical Properties of Diversity Factors for Probabilistic Loading of Distribution Transformers, IEEE 39th North American Power Symposium, (2007). [Google Scholar]
  12. Kaykahie S., Kowsari Movahed S., A New Approach for Calculating Load and Loss Factor Base on a New Approach for Calculating Load and Loss Factor Base on Consumer Data with Fuzzy Modelling, CIRED 22nd International Conference on Electricity Distribution, (2013). [Google Scholar]
  13. Tong X., Guo C., Yang X., Chen C., Research on Characteristics of Electric Vehicle Charging Load and Distribution Network Supportability, IEEE PES Asia- Pacific Power and Energy Conference, (2016). [Google Scholar]
  14. Chukwu U.C., Nworgu O.A., Dike D.O., Impact of V2G penetration on Distribution System Components Using Diversity Factor, >IEEE SOUTHEASTCON, (2014). [Google Scholar]
  15. Ciura S., Kocot H., Rzeczywiste i obliczeniowe obciążenia poszczególnych elementów sieci, zasilających odbiorców bytowych, przylączonych do sieci niskiego napięcia., Rynek energii elektrycznej. Aktualneproblemy energetyki., P. Pijarski, Z. Polecki, Eds., Politechnika Lubelska, pp. 33–42, (2018). [Google Scholar]
  16. Ciura S., Kubek P., Wyznaczanie obciążzeń maksymalnych w sieciach nn - teoria i praktyka, Rynek energii elektrycznej. Rozwoj i funkcjonowanie rynkow energii. Z. Polecki, P. Pijarski, Eds., Politechnika Lubelska, pp. 7–18, (2016). [Google Scholar]
  17. N SEP-E-002. Instalacje elektryczne w obiektach budowlanych. Instalacje elektryczne w budynkach mieszkalnych., COSiW SEP, (2003). [Google Scholar]
  18. Efron B., Bootstrap Methods: Another Look at the Jackknife, The Annals of Statistics, vol .7, pp. 1–26, (1979). [Google Scholar]
  19. Singh K., Xie M., Bootstrap: A Statistical Method, Rutgers University, (2008). [Google Scholar]
  20. Efron B., Estimating the Error Rate of a Prediction Rule: Improvement on Cross-validation, Journal of the American Statistical Association, vol. 78, (1983). [Google Scholar]
  21. Fox J., Applied Regression Analysis and Generalized Linear Models, SAGE Publications Inc., (2008). [Google Scholar]
  22. Davidson R., MacKinnon J., Bootstrap tests: how many bootstraps?, Econometric Reviews - Taylor & Francis Journals, vol. 191, (2000). [Google Scholar]
  23. Efron B., Tibshirani R., An Introdution to the Bootstrap, Chapman ’ Hall, 1993). [Google Scholar]
  24. Czarnecki L., Meta-theory of electric powers and present state of power theory of circuits with periodic voltages and currents, Przeglqd Elektrotechniczny no . 6 , pp .26–31, (2013). [Google Scholar]
  25. Bielecki S., Skoczkowski T., An enhanced concept of Q-power management, Energy no . 162 , pp .335–353, (2018). [CrossRef] [Google Scholar]

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