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 paper presents the design of the virtual instrument working in the real-time mode, run on the computer equipped with the dual-core processor. The new hardware solution for the multi-core processing and its importance for the instrument design is presented. The software support for such a computation using the National Instruments LabVIEW environment is described in detail. Description of the virtual instrument implementation of the parallel processing technique using two processor cores is presented. Finally, the results of the instrument's analysis and the comparison with the single core version are included. The paper is supped up with conclusions and the future prospects.

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 article presents the parametric method for functional testing of virtual instruments. The process of instrument design and implementation has been combined with the process of its testing. The possibilities of using the parametric method for functional testing in the process of design and verification of correct operation of the virtual instrument have been also presented.

The paper presents the application of the Wavelet Transform (WT) analysis to identify electrical appliances operating in the end-user household. The generic approach to the non-intrusive devices identification, i.e. using aggregated current and voltage signals is described. Next, the novel approach to extract features from the signals using WT coefficients is introduced. This allows for monitoring the appliance in the transient state, during the on-off event or while changing its mode (in the multi-state devices). This approach may replace or support the traditional steady-state analysis. Features extracted during switching the specific appliance in the laboratory conditions are then used to train the artificial intelligence-based classifier. It is further responsible for identifying the devices in the on-line mode. As the classifier, the random forest was applied. Its evaluation on the available data proves the usefulness of the proposed approach.