Nonintrusive appliance load monitoring (NIALM) allows disaggregation of total electricity consumption into particular appliances in domestic or industrial environments. NIALM systems operation is based on processing of electrical signals acquired at one point of a monitored area. The main objective of this paper was to present the state-of-the-art in NIALM technologies for the smart home. This paper focuses on sensors and measurement methods. Different intelligent algorithms for processing signals have been presented. Identification accuracy for an actual set of appliances has been compared. This article depicts the architecture of a unique NIALM laboratory, presented in detail. Results of developed NIALM methods exploiting different measurement data are discussed and compared to known methods. New directions of NIALM research are proposed.

The aim of appliance identification methods is to get electrical energy consumption at appliance level based on aggregate measurements from a single energy meter. Disaggregation of measurements from a single meter allow to reduce the costs of the hardware part of the energy management systems. The article presents results of home appliances identification based on active power measurements. The Factorial Hidden Markov Model is applied to identify different appliances in the same time. Independent changes in active power of every appliance is described by each Markov chain. Having measurements of active power from single meter it is necessary to compute hidden variables defining states of appliance. The Additive Factorial Approximate MAP algorithm allows to designate states of each appliance. Moreover, an analysis of available solutions in terms of measurement frequency and appliance mathematical modeling is presented. We present the flowchart of the prototype appliance identification system with low measurement frequency energy meter. In the experimental part of the article, results of the selected home appliances identification are presented. Based on the results we conclude that probabilistic models of appliances allow to identify appliances working simultaneously.

The paper presents research on a modelling of design measuring systems (MS) with time limited dataflow. A new method of describing MS model based on Petri nets and CPN tools environment is proposed in the article. The article provides an overall description of informal and formal methods, which can be used while designing systems

The paper presents application of Petri Nets to the modeling of the data acquisition in the distributed measurement system. The main advantage of the model is the consideration of the time dependencies of the system. Details of the model and tools used to its design are presented. Experiments using different computer configurations and integrated programming environments are described. Verification of the model against measurements of the real system are included.