30-05-2012, 03:28 PM
Load Identification of Non-intrusive Load-monitoring System in Smart Home
[attachment=23564]
Abstract:
In response to the governmental policy of saving energy sources and reducing CO2, and carry out the resident quality of local; this paper proposes a new method for a non-intrusive load-monitoring (NILM) system in smart home to implement the load identification of electric equipments and establish the electric demand management. Non-intrusive load-monitoring techniques were often based on power signatures in the past, these techniques are necessary to be improved for the results of reliability and accuracy of recognition. By using neural network (NN) in combination with genetic programming (GP) and turn-on transient energy analysis.
Introduction
Smart home provides an integrated service in intelligent residences for health care, human life, residence safety and environment of leisure in a community; for examples, security service, monitoring and management system service, logistics service, medical care service, distance e-learning service, leisure service, e-commerce service, and etc. The quality of human life is gradually emphasized by peoples; the demands of resident services for user’s own need are increasing. The smart home is an emphasis on quality of residence. The peoples can enjoy the professional and considerate resident services, medical care with comfortable, carefree residence space and happiness environments in the smart home by using innovative techniques.
Review of Related Studies
Due to the importance and difference of recognition accuracy of power signatures, several previous studies have addressed the load identification of power signatures in NILM. Hart [7] proposed a load identification method that examined the steady-state behavior of loads. Hart conceptualized a finite state machine to represent a single appliance in which power consumption varied discretely with each step change. The method performs well. However, it has the limitations of the method. For example, small appliances and appliances, which are always on or non-discrete changes in power, should not be chosen as targets for the method [4], [7].
Data Preparation
Figure 1 schematically illustrates the overall scheme in the NILM system of smart home. One-phase electricity powers the loads, which are representative of important load classes in a residential building. A dedicated computer connected to the circuit breaker panel controls the operation of each load. The local computer can also be programmed to stimulate various end-use scenarios. The work presented in this paper is load recognition using neural networks and the employment of features to estimate the energy consumption of major loads.
Conclusions
The results of analysis for NILM system can identify various loads of home and to know the condition of use for loads including of the electric power demands, names or items, time of use and overloaded capacities of loads, etc. The users of home can be reminded to save energy by these results. Besides, some related policies of saving energy, reducing CO2, health and safety care for hidden elderly and the efficiency of electric appliances can be established and planed by these results of smart home.
[attachment=23564]
Abstract:
In response to the governmental policy of saving energy sources and reducing CO2, and carry out the resident quality of local; this paper proposes a new method for a non-intrusive load-monitoring (NILM) system in smart home to implement the load identification of electric equipments and establish the electric demand management. Non-intrusive load-monitoring techniques were often based on power signatures in the past, these techniques are necessary to be improved for the results of reliability and accuracy of recognition. By using neural network (NN) in combination with genetic programming (GP) and turn-on transient energy analysis.
Introduction
Smart home provides an integrated service in intelligent residences for health care, human life, residence safety and environment of leisure in a community; for examples, security service, monitoring and management system service, logistics service, medical care service, distance e-learning service, leisure service, e-commerce service, and etc. The quality of human life is gradually emphasized by peoples; the demands of resident services for user’s own need are increasing. The smart home is an emphasis on quality of residence. The peoples can enjoy the professional and considerate resident services, medical care with comfortable, carefree residence space and happiness environments in the smart home by using innovative techniques.
Review of Related Studies
Due to the importance and difference of recognition accuracy of power signatures, several previous studies have addressed the load identification of power signatures in NILM. Hart [7] proposed a load identification method that examined the steady-state behavior of loads. Hart conceptualized a finite state machine to represent a single appliance in which power consumption varied discretely with each step change. The method performs well. However, it has the limitations of the method. For example, small appliances and appliances, which are always on or non-discrete changes in power, should not be chosen as targets for the method [4], [7].
Data Preparation
Figure 1 schematically illustrates the overall scheme in the NILM system of smart home. One-phase electricity powers the loads, which are representative of important load classes in a residential building. A dedicated computer connected to the circuit breaker panel controls the operation of each load. The local computer can also be programmed to stimulate various end-use scenarios. The work presented in this paper is load recognition using neural networks and the employment of features to estimate the energy consumption of major loads.
Conclusions
The results of analysis for NILM system can identify various loads of home and to know the condition of use for loads including of the electric power demands, names or items, time of use and overloaded capacities of loads, etc. The users of home can be reminded to save energy by these results. Besides, some related policies of saving energy, reducing CO2, health and safety care for hidden elderly and the efficiency of electric appliances can be established and planed by these results of smart home.