@inproceedings{ ruzzelli_real-time_2010,
  title = {Real-Time Recognition and Profiling of Appliances through a Single Electricity Sensor},
  doi = {10.1109/SECON.2010.5508244},
  abstract = {Sensing, monitoring and actuating systems are expected to play a key role in reducing buildings overall energy consumption. Leveraging sensor systems to support energy efficiency in buildings poses novel research challenges in monitoring space usage, controlling devices, interfacing with smart energy meters and communicating with the energy grid. In the attempt of reducing electricity consumption in buildings, identifying individual sources of energy consumption is key to generate energy awareness and improve efficiency of available energy resources usage. Previous work studied several non-intrusive load monitoring techniques to classify appliances; however, the literature lacks of an comprehensive system that can be easily installed in existing buildings to empower users profiling, benchmarking and recognizing loads in real-time. This has been a major reason holding back the practice adoption of load monitoring techniques. In this paper we present {RECAP}: {RECognition} of electrical Appliances and Profiling in real-time. {RECAP} uses a single wireless energy monitoring sensor easily clipped to the main electrical unit. The energy monitoring unit transmits energy data wirelessly to a local machine for data processing and storage. The {RECAP} system consists of three parts: (1) Guiding the user for profiling electrical appliances within premises and generating a database of unique appliance signatures; (2) Using those signatures to train an artificial neural network that is then employed to recognize appliance activities (3) Providing a Load descriptor to allow peer appliance benchmarking. {RECAP} addresses the need of an integrated and intuitive tool to empower building owners with energy awareness. Enabling real-time appliance recognition is a stepping-stone towards reducing energy consumption and allowing a number of major applications including load-shifting techniques, energy expenditure breakdown per appliance, detection of power hungry and faulty appliances, and recogn- - ition of occupant activity. This paper describes the system design and performance evaluation in domestic environment.},
  booktitle = {2010 7th Annual {IEEE} Communications Society Conference on Sensor Mesh and Ad Hoc Communications and Networks ({SECON})},
  author = {Ruzzelli, AG. and Nicolas, C. and Schoofs, A and O'Hare, G. M P},
  month = {June},
  year = {2010},
  keywords = {Control systems, Electricity consumption, Energy efficiency, Home appliances, Intelligent sensors, Monitoring, Power generation, {RECAP}, Sensor systems, Watthour meters, artificial neural network, domestic appliances, electric sensing devices, electrical products, energy awareness, energy consumption, energy expenditure breakdown, energy grid, leveraging sensor systems, load monitoring techniques, load-shifting techniques, monitoring space usage, power consumption, power meters, real-time appliance recognition, real-time recognition, recognition of electrical appliances and profiling, single electricity sensor, single wireless energy monitoring sensor, smart energy meters},
  pages = {1--9}
}

{"_id":"CDdjZkJyk9B8AbofW","authorIDs":[],"author_short":["Ruzzelli, A.","Nicolas, C.","Schoofs, A.","O'Hare","P, G.<nbsp>M."],"bibbaseid":"ruzzelli-nicolas-schoofs-ohare-p-realtimerecognitionandprofilingofappliancesthroughasingleelectricitysensor-2010","bibdata":{"abstract":"Sensing, monitoring and actuating systems are expected to play a key role in reducing buildings overall energy consumption. Leveraging sensor systems to support energy efficiency in buildings poses novel research challenges in monitoring space usage, controlling devices, interfacing with smart energy meters and communicating with the energy grid. In the attempt of reducing electricity consumption in buildings, identifying individual sources of energy consumption is key to generate energy awareness and improve efficiency of available energy resources usage. Previous work studied several non-intrusive load monitoring techniques to classify appliances; however, the literature lacks of an comprehensive system that can be easily installed in existing buildings to empower users profiling, benchmarking and recognizing loads in real-time. This has been a major reason holding back the practice adoption of load monitoring techniques. In this paper we present RECAP: RECognition of electrical Appliances and Profiling in real-time. RECAP uses a single wireless energy monitoring sensor easily clipped to the main electrical unit. The energy monitoring unit transmits energy data wirelessly to a local machine for data processing and storage. The RECAP system consists of three parts: (1) Guiding the user for profiling electrical appliances within premises and generating a database of unique appliance signatures; (2) Using those signatures to train an artificial neural network that is then employed to recognize appliance activities (3) Providing a Load descriptor to allow peer appliance benchmarking. RECAP addresses the need of an integrated and intuitive tool to empower building owners with energy awareness. Enabling real-time appliance recognition is a stepping-stone towards reducing energy consumption and allowing a number of major applications including load-shifting techniques, energy expenditure breakdown per appliance, detection of power hungry and faulty appliances, and recogn- - ition of occupant activity. This paper describes the system design and performance evaluation in domestic environment.","author":["Ruzzelli, AG.","Nicolas, C.","Schoofs, A","O'Hare","P, G. M"],"author_short":["Ruzzelli, A.","Nicolas, C.","Schoofs, A.","O'Hare","P, G.<nbsp>M."],"bibtex":"@inproceedings{ ruzzelli_real-time_2010,\n title = {Real-Time Recognition and Profiling of Appliances through a Single Electricity Sensor},\n doi = {10.1109/SECON.2010.5508244},\n abstract = {Sensing, monitoring and actuating systems are expected to play a key role in reducing buildings overall energy consumption. Leveraging sensor systems to support energy efficiency in buildings poses novel research challenges in monitoring space usage, controlling devices, interfacing with smart energy meters and communicating with the energy grid. In the attempt of reducing electricity consumption in buildings, identifying individual sources of energy consumption is key to generate energy awareness and improve efficiency of available energy resources usage. Previous work studied several non-intrusive load monitoring techniques to classify appliances; however, the literature lacks of an comprehensive system that can be easily installed in existing buildings to empower users profiling, benchmarking and recognizing loads in real-time. This has been a major reason holding back the practice adoption of load monitoring techniques. In this paper we present {RECAP}: {RECognition} of electrical Appliances and Profiling in real-time. {RECAP} uses a single wireless energy monitoring sensor easily clipped to the main electrical unit. The energy monitoring unit transmits energy data wirelessly to a local machine for data processing and storage. The {RECAP} system consists of three parts: (1) Guiding the user for profiling electrical appliances within premises and generating a database of unique appliance signatures; (2) Using those signatures to train an artificial neural network that is then employed to recognize appliance activities (3) Providing a Load descriptor to allow peer appliance benchmarking. {RECAP} addresses the need of an integrated and intuitive tool to empower building owners with energy awareness. Enabling real-time appliance recognition is a stepping-stone towards reducing energy consumption and allowing a number of major applications including load-shifting techniques, energy expenditure breakdown per appliance, detection of power hungry and faulty appliances, and recogn- - ition of occupant activity. This paper describes the system design and performance evaluation in domestic environment.},\n booktitle = {2010 7th Annual {IEEE} Communications Society Conference on Sensor Mesh and Ad Hoc Communications and Networks ({SECON})},\n author = {Ruzzelli, AG. and Nicolas, C. and Schoofs, A and O'Hare, G. M P},\n month = {June},\n year = {2010},\n keywords = {Control systems, Electricity consumption, Energy efficiency, Home appliances, Intelligent sensors, Monitoring, Power generation, {RECAP}, Sensor systems, Watthour meters, artificial neural network, domestic appliances, electric sensing devices, electrical products, energy awareness, energy consumption, energy expenditure breakdown, energy grid, leveraging sensor systems, load monitoring techniques, load-shifting techniques, monitoring space usage, power consumption, power meters, real-time appliance recognition, real-time recognition, recognition of electrical appliances and profiling, single electricity sensor, single wireless energy monitoring sensor, smart energy meters},\n pages = {1--9}\n}","bibtype":"inproceedings","booktitle":"2010 7th Annual IEEE Communications Society Conference on Sensor Mesh and Ad Hoc Communications and Networks (SECON)","doi":"10.1109/SECON.2010.5508244","id":"ruzzelli_real-time_2010","key":"ruzzelli_real-time_2010","keywords":"Control systems, Electricity consumption, Energy efficiency, Home appliances, Intelligent sensors, Monitoring, Power generation, RECAP, Sensor systems, Watthour meters, artificial neural network, domestic appliances, electric sensing devices, electrical products, energy awareness, energy consumption, energy expenditure breakdown, energy grid, leveraging sensor systems, load monitoring techniques, load-shifting techniques, monitoring space usage, power consumption, power meters, real-time appliance recognition, real-time recognition, recognition of electrical appliances and profiling, single electricity sensor, single wireless energy monitoring sensor, smart energy meters","month":"June","pages":"1--9","title":"Real-Time Recognition and Profiling of Appliances through a Single Electricity Sensor","type":"inproceedings","year":"2010","bibbaseid":"ruzzelli-nicolas-schoofs-ohare-p-realtimerecognitionandprofilingofappliancesthroughasingleelectricitysensor-2010","role":"author","urls":{},"keyword":["Control systems","Electricity consumption","Energy efficiency","Home appliances","Intelligent sensors","Monitoring","Power generation","RECAP","Sensor systems","Watthour meters","artificial neural network","domestic appliances","electric sensing devices","electrical products","energy awareness","energy consumption","energy expenditure breakdown","energy grid","leveraging sensor systems","load monitoring techniques","load-shifting techniques","monitoring space usage","power consumption","power meters","real-time appliance recognition","real-time recognition","recognition of electrical appliances and profiling","single electricity sensor","single wireless energy monitoring sensor","smart energy meters"],"downloads":0,"html":""},"bibtype":"inproceedings","biburl":"http://bibbase.org/zotero/alspereira","creationDate":"2014-12-11T16:27:23.892Z","downloads":0,"keywords":["control systems","electricity consumption","energy efficiency","home appliances","intelligent sensors","monitoring","power generation","recap","sensor systems","watthour meters","artificial neural network","domestic appliances","electric sensing devices","electrical products","energy awareness","energy consumption","energy expenditure breakdown","energy grid","leveraging sensor systems","load monitoring techniques","load-shifting techniques","monitoring space usage","power consumption","power meters","real-time appliance recognition","real-time recognition","recognition of electrical appliances and profiling","single electricity sensor","single wireless energy monitoring sensor","smart energy meters"],"search_terms":["real","time","recognition","profiling","appliances","through","single","electricity","sensor","ruzzelli","nicolas","schoofs","o'hare","p"],"title":"Real-Time Recognition and Profiling of Appliances through a Single Electricity Sensor","year":2010,"dataSources":["ndsqabutJ3xHr3adK"]}