Non-intrusive load monitoring algorithm based on household electricity use habits

Abstract

The construction of smart grid is an important part of improving the utilization rate of electric energy. As an important way for the construction of smart grid, non-intrusive load decomposition methods have been extensively studied. In this type of method, limited by transmission cost and network bandwidth, low-frequency data has been widely used in practical applications. However, the accuracy of device identification in this case faces challenges. Due to the relatively single characteristics of low-frequency data, it is difficult to express the operating status of complex electrical appliances, resulting in low decomposition performance. In this paper, a non-intrusive load is proposed based on household electrical habits by studying the relationship between household electricity consumption habits and load status decomposition method. The Gaussian mixture model and time information are used to model the probability distribution of the electrical appliance state. This probability distribution is then used as the observation probability distribution of the factor hidden Markov model. In such a way, the BH-FHMM model is proposed. Finally, load decomposition is carried out through the load decomposition process of the FHMM model. In order to verify the performance of the proposed method, an experimental comparison is conducted based on the REDD data set. According to the results, a significant improvement in equipment recognition accuracy is obtained.

Resilient data-driven non-intrusive load monitoring for efficient energy management using machine learning techniques

“…By incorporating appliance state transitions, HMMs can accurately infer the appliance responsible for specific energy consumption patterns. This approach has demonstrated promising results in appliance-level energy disaggregation [16] and has become a foundational technique in NILM research. Similarly, [17] focuses on exploiting the sparsity property of hidden Markov models (HMMs) to perform online real-time non-intrusive load monitoring (NILM).…”

Nonintrusive Load Identification for Industrial Users Integrated with LSQR and Sequential Leader Clustering

“…In both sides of ( 9), multiply U on the left to get (10). On the other hand, apply the Hermittransform to both sides of (11)…”

“…In order to verify the effectiveness of the proposed algorithm, considering a single sample, the load identification models in [11] (FHMM) and in [12] (ANN) are selected to When unknown transient events occur on the bus-side, the related feature indices are extracted and normalized, and then they are classified by the clustering algorithm, so as to identify the specific load switching operation corresponding to the event.…”

“…Presently, some research results probe into deep learning (DL) intelligent algorithms for NILM in the steady-state process, such as hidden Markov model [10,11], arti cial neural network (ANN) model [12,13], and graph signal processing developed approach [14,15]. In [10,11], the factorial hidden Markov model (FHMM) is considered, and it is indicated to outperform the other unsupervised disaggregation methods. In [12,13], the DL model on ANN algorithm is also investigated and has high effectiveness and accuracy in analysis of power consumption behavior for load monitoring.…”

Towards energy‐efficient smart homes via precise nonintrusive load disaggregation based on hybrid ANN–PSO

“…The practical data of six smart homes were used to show the effectiveness of this method, and the results were compared with other methods 14 . The human FHMM was proposed before 15 based on consumers' power consumption as a nonintrusive load disaggregation method to improve the accuracy of the NILM method. This algorithm was tested on REDD datasets and compared to the traditional FHMM method.…”