@inproceedings{ baranski_genetic_2004,
  title = {Genetic algorithm for pattern detection in {NIALM} systems},
  volume = {4},
  doi = {10.1109/ICSMC.2004.1400878},
  abstract = {Nonintrusive appliance load monitoring systems ({NIALM}) require sufficient accurate total load data to separate the load into its major appliances. The most available solutions separate the whole electric energy consumption based on the measurement of all three voltages and currents. Aside from the cost for special measuring devices, the intrusion into the local installation is the main problem for reaching a high market distribution. The use of standard digital electricity meters could avoid this problem with loss of information in the measured data. This paper presents a new {NIALM} approach to analyse data, collected form a standard digital electricity meter. To disaggregate the consumption of the entire active power into its major electrical end uses, an algorithm consisting of fuzzy clustering methods, a genetic algorithm and a dynamic programming approach is presented.},
  booktitle = {2004 {IEEE} International Conference on Systems, Man and Cybernetics},
  author = {Baranski, M. and Voss, J.},
  month = {October},
  year = {2004},
  keywords = {Costs, Current measurement, Electric variables measurement, Energy measurement, Home appliances, Monitoring, Watthour meters, automatic meter reading, data analysis, digital electricity meters, dynamic programming, electric energy consumption, energy consumption, fuzzy clustering method, fuzzy systems, genetic algorithm, genetic algorithms, high market distribution, load (electric), nonintrusive appliance load monitoring system, pattern clustering, pattern detection, power consumption, voltage},
  pages = {3462--3468 vol.4}
}

{"_id":"YSsgHELrghhsbgQxE","authorIDs":[],"author_short":["Baranski, M.","Voss, J."],"bibbaseid":"baranski-voss-geneticalgorithmforpatterndetectioninnialmsystems-2004","bibdata":{"abstract":"Nonintrusive appliance load monitoring systems (NIALM) require sufficient accurate total load data to separate the load into its major appliances. The most available solutions separate the whole electric energy consumption based on the measurement of all three voltages and currents. Aside from the cost for special measuring devices, the intrusion into the local installation is the main problem for reaching a high market distribution. The use of standard digital electricity meters could avoid this problem with loss of information in the measured data. This paper presents a new NIALM approach to analyse data, collected form a standard digital electricity meter. To disaggregate the consumption of the entire active power into its major electrical end uses, an algorithm consisting of fuzzy clustering methods, a genetic algorithm and a dynamic programming approach is presented.","author":["Baranski, M.","Voss, J."],"author_short":["Baranski, M.","Voss, J."],"bibtex":"@inproceedings{ baranski_genetic_2004,\n title = {Genetic algorithm for pattern detection in {NIALM} systems},\n volume = {4},\n doi = {10.1109/ICSMC.2004.1400878},\n abstract = {Nonintrusive appliance load monitoring systems ({NIALM}) require sufficient accurate total load data to separate the load into its major appliances. The most available solutions separate the whole electric energy consumption based on the measurement of all three voltages and currents. Aside from the cost for special measuring devices, the intrusion into the local installation is the main problem for reaching a high market distribution. The use of standard digital electricity meters could avoid this problem with loss of information in the measured data. This paper presents a new {NIALM} approach to analyse data, collected form a standard digital electricity meter. To disaggregate the consumption of the entire active power into its major electrical end uses, an algorithm consisting of fuzzy clustering methods, a genetic algorithm and a dynamic programming approach is presented.},\n booktitle = {2004 {IEEE} International Conference on Systems, Man and Cybernetics},\n author = {Baranski, M. and Voss, J.},\n month = {October},\n year = {2004},\n keywords = {Costs, Current measurement, Electric variables measurement, Energy measurement, Home appliances, Monitoring, Watthour meters, automatic meter reading, data analysis, digital electricity meters, dynamic programming, electric energy consumption, energy consumption, fuzzy clustering method, fuzzy systems, genetic algorithm, genetic algorithms, high market distribution, load (electric), nonintrusive appliance load monitoring system, pattern clustering, pattern detection, power consumption, voltage},\n pages = {3462--3468 vol.4}\n}","bibtype":"inproceedings","booktitle":"2004 IEEE International Conference on Systems, Man and Cybernetics","doi":"10.1109/ICSMC.2004.1400878","id":"baranski_genetic_2004","key":"baranski_genetic_2004","keywords":"Costs, Current measurement, Electric variables measurement, Energy measurement, Home appliances, Monitoring, Watthour meters, automatic meter reading, data analysis, digital electricity meters, dynamic programming, electric energy consumption, energy consumption, fuzzy clustering method, fuzzy systems, genetic algorithm, genetic algorithms, high market distribution, load (electric), nonintrusive appliance load monitoring system, pattern clustering, pattern detection, power consumption, voltage","month":"October","pages":"3462--3468 vol.4","title":"Genetic algorithm for pattern detection in NIALM systems","type":"inproceedings","volume":"4","year":"2004","bibbaseid":"baranski-voss-geneticalgorithmforpatterndetectioninnialmsystems-2004","role":"author","urls":{},"keyword":["Costs","Current measurement","Electric variables measurement","Energy measurement","Home appliances","Monitoring","Watthour meters","automatic meter reading","data analysis","digital electricity meters","dynamic programming","electric energy consumption","energy consumption","fuzzy clustering method","fuzzy systems","genetic algorithm","genetic algorithms","high market distribution","load (electric)","nonintrusive appliance load monitoring system","pattern clustering","pattern detection","power consumption","voltage"],"downloads":0,"html":""},"bibtype":"inproceedings","biburl":"http://bibbase.org/zotero/alspereira","creationDate":"2014-12-11T16:27:23.934Z","downloads":0,"keywords":["costs","current measurement","electric variables measurement","energy measurement","home appliances","monitoring","watthour meters","automatic meter reading","data analysis","digital electricity meters","dynamic programming","electric energy consumption","energy consumption","fuzzy clustering method","fuzzy systems","genetic algorithm","genetic algorithms","high market distribution","load (electric)","nonintrusive appliance load monitoring system","pattern clustering","pattern detection","power consumption","voltage"],"search_terms":["genetic","algorithm","pattern","detection","nialm","systems","baranski","voss"],"title":"Genetic algorithm for pattern detection in NIALM systems","year":2004,"dataSources":["ndsqabutJ3xHr3adK"]}