Weitere Artikel dieser Ausgabe durch Wischen aufrufen
This paper is intended to explain how the possibilities of enabling technologies (advanced metering infrastructures) can be expanded on to evaluate end uses at the demand-side level. For example, these data allow validating the effective response to market prices (energy markets) or system events (demand response), and besides, the possibilities that energy efficiency offers (in capacity markets), mainly under the supervision of a load aggregator. Hilbert transform properties along with other mathematical tools are used to extract the characteristics of the more suitable uses for demand response policies from the aggregated load demand of the user. This is achieved without complex statistical analysis of the demand loads. The tool filters pulse waveforms (in this case, the components of daily demand) and provides the aggregator the main characteristics of load, both in normal state or under response to system events or market prices.
Almeida, A. T. de, & Fonseca, P. (2010). Residential monitoring to decrease energy use and carbon emissions in Europe. EACI-Intelligent Energy Europe project report, 2006–2008, ISR-University of Coimbra.
Bertoldi, P., Hirl, B., Labianca, N. Energy efficiency status report. Joint Research Centre, 2012. http://iet.jrc.ec.europa.eu/energyefficiency/sites/energyefficiency/files/energy-efficiency-status-report-2012.pdf. Accessed 2 May 2013.
Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (1994). Time series analysis: Forecasting and control (3rd ed.). Upper Saddle River: Prentice-Hall. MATH
Browne, T. J., Vittal, V., Heydt, G. T., & Messina, A. R. (2008). A comparative assessment of two techniques for modal identification from power system measurements. IEEE Transactions on Power Systems, 23(3), 1408–1415. CrossRef
Chang, H. H. (2012). Non-intrusive demand monitoring and load identification for energy management systems based on transient feature analyses. Energies, 5, 4569–4589. CrossRef
Deering, R., & Kaiser, J. F. (2005). The use of a masking signal to improve empirical mode decomposition. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, 4, 485–488.
EIA. (2001). End-use consumption electricity 2001. http://www.eia.gov/emeu/recs/recs2001/enduse2001/enduse2001.html. Accessed 2 May 2013.
Elliott, R. J., Aggoun, L., & Moore, J. B. (1995). Hidden Markov models. New York: Springer Science+Business Media. MATH
Johnson Controls. (2010). Energy performance contracting in the European Union: Introduction, barriers and prospects. Milwaukee: Johnson Controls.
European Commission. (2011). Energy efficiency plan 2011, COM (2011) 109 final. http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=COM:2011:0109:FIN:EN:PDF. Accessed 4 Feb 2013.
European Parliament and Council. Directive 2012/27/EU of the European Parliament and of the council of October 2012 on energy efficiency, amending directives 2009/125/EC and 2010/30/EU. http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2012:315:0001:0056:EN:PDF. Accessed Nov 2013.
Faruqui, A., Harris, D., & Hledick, R. (2010). Unlocking the €53 Billion Savings from Smart Meters in the EU: How increasing the adoption of dynamic tariffs could make or break the EU’s smart grid investment. Energy Policy, 38(10), 6222–6231. CrossRef
Faruqui, A., Sergici, S., & Akaba, L. (2013). Dynamic pricing of electricity for residential customers: The evidence from Michigan. Energy Efficiency, 6, 571–584. CrossRef
Gabaldón, A., Guillamón, A., Ruiz, M. C., Valero, S., Ortiz, M., & Alvarez, C. (2010). Development of a methodology for clustering electricity-price series to improve customer response initiatives. IET Generation, Transmission & Distribution, 4(6), 706–715. CrossRef
Goldman, Ch., Reid, M., Levy, R., Silverstein, A. (2010). Coordination of energy efficiency and demand response, LBNL report 3044E.
Gomatom, K., & Homes, C. Evaluation of NILMs technologies for electric load disaggregation. 1st International Workshop on Non-Intrusive Load Monitoring, Pittsburgh, PA, USA, May 2012.
Greenbang. (2012). Europe’s smart meter outlook for 2020 a $25 billion market. http://www.greenbang.com/wp-content/uploads/2012/02/Smart-Meter-Outlook-2020.pdf. Accessed 15 Oct 2013.
Hart, G. W. (1992). Nonintrusive appliance load monitoring. Proceedings of the IEEE, 80(12), 1870–1891. CrossRef
Huang, N. E., Shen, Z., Long, S. R., & Manli, C. W. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis. Procedures of the Royal Society of London, 454(1971), 903–995. MATH
Jenkins, C., Neme, C., & Enterline, S. (2011). Energy efficiency as a resource in the ISO New England capacity market. Energy Efficiency, 4(1), 31–42. CrossRef
Kim, H., Marwah, M., Arlitt, M., Lyon, G., Han, J. (2011). Unsupervised disaggregation of low frequency power measurements. Proceedings of the 11th SIAM Conference on Data Mining (pp. 747–758). Arizona: USA.
Kolter, J. Z., & Johnson, M. J. (2011). REDD: A public data set for energy disaggregation research. Proceedings of the SustKDD workshop on Data Mining Applications in Sustainability. San Diego, CA, USA.
Koponen, P. (2012). Measurements and models of electricity demand responses. Research Report VTT-R-09198-11, VTT 2012. http://www.cleen.fi/en/sgem/public_deliverables#Technical. Accessed 2 May 2013.
Liang, J., Simon, K., Kendall, G., & Cheng, J. (2010a). Load signature study. Part I: basic concept, structure and methodology. IEEE Transactions on Power Delivery, 25(2), 551–560. CrossRef
Liang, J., Simon, K., Kendall, G., & Cheng, J. (2010b). Load signature study. Part II: Disaggregation framework, simulation, and applications. IEEE Transactions on Power Delivery, 25(2), 561–569. CrossRef
Piette, M. A., Watson, D., Motegi, N., Kiliccote, S. (Lawrence Berkeley National Laboratory). (2007). Automated critical peak pricing field tests: 2006 pilot program description and results. California Energy Commission, PIER Energy Systems Integration Research, Program. CEC-500-03-026, 2007.
Poularikas, A. D. (1999). The handbook of formulas and tables for signal processing. Boca Raton: CRC. MATH
Senroy, N., Suryanarayanan, S., & Ribeiro, P. F. (2007). An improved Hilbert–Huang method for analysis of time-varying waveforms in power quality. IEEE Transactions on Power Systems, 22(4), 1843–1850. CrossRef
Zeifman, M., & Roth, K. (2011). Nonintrusive appliance load monitoring: Review and outlook. IEEE Transactions on Consumer Electronics, 57(1), 76–84. CrossRef
- Disaggregation of the electric loads of small customers through the application of the Hilbert transform
- Springer Netherlands
Fallstudie Überschwemmungskarten/© Thaut Images | Fotolia