Load Identification of Non-intrusive Load-monitoring System in Smart Home
نویسنده
چکیده
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, this study attempts to identify load demands and improve recognition accuracy of non-intrusive loadmonitoring results. The turn-on transient energy signature can improve the efficiency of load identification and computational time under multiple operations. Key-Words: load identification, artificial neural networks, non-intrusive load monitoring, turn-on transient energy analysis, smart home.
منابع مشابه
An efficient and inexpensive method for activity recognition within a smart home based on load signatures of appliances
With the increasing demand in terms of non-intrusive appliance load monitoring (NIALM), more and more smart meters and smart analyzers were released on the market to extract well-defined load signatures and/or for performing autonomously the various monitoring operations as needed. Nevertheless, this hardware proves to be very expensive and not necessarily accessible to all. Moreover, most appl...
متن کاملTowards Automated Non-Intrusive Load Monitoring Performance Evaluation
In the past couple of years Non-Intrusive Load Monitoring has been gaining special attention from the research community playing an important role in the solution of problems related to energy sustainability and smart-grids. However despite its notoriety, until recently no efforts were made towards having formal methods for evaluating its performance. In this paper we present our hardware-softw...
متن کاملAchieving Differential Privacy against Non-Intrusive Load Monitoring in Smart Grid: a Fog Computing approach
Fog computing, a non-trivial extension of cloud computing to the edge of the network, has great advantage in providing services with a lower latency. In smart grid, the application of fog computing can greatly facilitate the collection of consumer’s fine-grained energy consumption data, which can then be used to draw the load curve and develop a plan or model for power generation. However, such...
متن کاملNon-Intrusive Demand Monitoring and Load Identification for Energy Management Systems Based on TransientFeature Analyses
Energy management systems strive to use energy resources efficiently, save energy, and reduce carbon output. This study proposes transient feature analyses of the transient response time and transient energy on the power signatures of non-intrusive demand monitoring and load identification to detect the power demand and load operation. This study uses the wavelet transform (WT) of the time-freq...
متن کاملLoad Identification in Neural Networks for a Non-intrusive Monitoring of Industrial Electrical Loads
This paper proposes the use of neural network classifiers to evaluate back propagation (BP) and learning vector quantization (LVQ) for feature selection of load identification in a non-intrusive load monitoring (NILM) system. To test the performance of the proposed approach, data sets for electrical loads were analyzed and established using a computer supported program Electromagnetic Transient...
متن کامل