ZigBee-Based Real-Time Energy Disaggregation System Using Factorial Hidden Markov Model

Non-Intrusive Load Monitoring (NILM) is an efficient way for energy disaggregation, as it can identify and monitor power consumption of downstream appliances or equipment from the aggregated data acquired from a single electricity metering point. There are many researches and applications of NILM in residential load disaggregation, but very few are applied on industrial load so far. This paper studies the industrial load disaggregation via NILM based on on-situ monitoring energy consumption data from one factory. The factorial hidden Markov model (FHMM) is applied for load disaggregation of five industrial equipment in the factory with the active power and reactive power data synchronously sampled at the rate of 1 Hz. This is the first attempt of using FHMM with both active power and reactive power as outputs for modeling industrial load in this field of NILM, and the promising results demonstrate its effectiveness for industrial load disaggregation.

A mixed-integer programming approach for industrial non-intrusive load monitoring

Unsupervised approach for load disaggregation with devices interactions

Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network

Hidden Markov models with factored Gaussian mixtures densities

Non-intrusive load monitoring (NILM) is to estimate individual appliance’s power consumption from aggregated smart meter data, which is useful for optimized energy management and provisioning of customized services. While deep learning (DL) has achieved state-of-the-art NILM performance, it is still constrained by the dependency on large amounts of data and intensive computations on training. In this paper, we propose a pre-training approach to address the generalization of DL models for NILM. We develop a meta-learning based approach and an ensemble learning based approach, which pre-train a base model and then fine-tune it with few-short learning when applied to an unknown dataset. The models are validated with two real-world datasets and shown to achieve a superior transferability performance compared with traditional DL and transfer learning methods.

This paper presents a complete real-time implementation of a Non-Intrusive Appliance Load Monitoring (NIALM) system that, is robust under residential voltage level fluctuations. Existing NIALM techniques rely on multiple measurements taken at high sampling rates, but, only have been proven in simulated environments without even considering the effect of residential voltage level fluctuations — which is a severe problem in power systems of most developing countries like Sri Lanka. In contrast, through the NIALM method proposed in this paper, accurate load monitoring results were obtained in realtime using only smart meter measurements taken at a low sampling rate from a real appliance setup under residential voltage level fluctuations. In the proposed NIALM method, initially in the learning phase, a properly constructed MATLABTM Graphical User Interface (GUI) was used to acquire signals of each appliance active power consumption and voltage levels. Then, obtained active power measurements were separated into subspace components (SCs) via the Karhunen Loeve' Expansion (KLE) while also taking the voltage variations into account. Using those SCs, a unique information rich appliance level signature database was constructed and it was then used to obtain the signatures for all possible device combinations. Next, a separate GUI was designed to identify the turned ON appliance combination in the current time window using the pre-constructed signature databases, after reading the total residential active power consumption and the supply voltage. To validate the proposed real-time NIALM implementation, data from a laboratory arrangement consisting of ten household appliances was used. From the results, it was found that the proposed method is capable of accurately identifying the turned on appliances even under severe residential supply voltage level fluctuations.

Amyotrophic lateral sclerosis (ALS) is a common disease among neurological disorders that can change the pattern of gait in human. One of the effective methods for recognition and analysis of gait patterns in ALS patients is utilizing stride interval time series. With proper preprocessing for removing unwanted artifacts from the raw stride interval times and then extracting meaningful features from these data, the factorial hidden Markov model (FHMM) was used to distinguish ALS patients from healthy subjects. The results of classification accuracy evaluated using the leave-one-out (LOO) cross-validation algorithm showed that the FHMM method provides better recognition of ALS and healthy subjects compared to standard HMM. Moreover, comparing our method with a state-of-the art method named least square support vector machine (LS-SVM) showed the efficiency of the FHMM in distinguishing ALS subjects from healthy ones.

It is more and more important to consider the dependence structure among multiple testings, especially for the genome-wide association studies (GWAS). The existing procedures, such as local index of significance (LIS) and pooled local index of significance (PLIS), were proposed to test hidden Markov model (HMM)-dependent hypotheses under the framework of compound decision theory, which was successfully applied to GWAS. However, the etiology of complex diseases is not only with respect to the genetic effects, but also the environmental factors. Failure to account for the covariates in multiple testing can produce misleading bias of the association of interest, or suffer from loss of testing efficiency. In this paper, we develop a covariate-adjusted multiple testing procedure, called covariate-adjusted local index of significance (CALIS), to account for the effects of environmental factors via a factorial hidden Markov model. The theoretical results show that our procedure can control the false discovery rate (FDR) at the nominal level and has the smallest false non-discovery rate (FNR) among all valid FDR procedures. We further demonstrate the advantage of our novel procedure over the existing procedures by simulation studies and a real data analysis.

Non-Intrusive Appliance Load Monitoring (NIALM) is a process for deducting what appliances are being used in a household by observing the total energy consumption over time. Such process makes it possible to provide energy management services to a large number of users without installation of per-appliance meters. This paper analyzes the performance of NIALM when used to provide real-time services. In addition, the paper studies how the performance of NIALM could be improved by dynamically selecting the disaggregation model on the basis of user feedback about the correctness of the disaggregation.

Knowing the power consumption of individual household appliances is useful for end-user as well as utilities. There are two ways for appliance load monitoring (ALM), namely intrusive load monitoring (ILM) and non-intrusive load monitoring (NILM). This paper focuses on the NILM approach, and discusses a simple yet effective method to improve its accuracy by constructing a better knowledge-base. The proposed methodology is initially verified with the simulation using the Reference Energy Disaggregation Data (REDD) dataset, and later tested on a lab-scale hardware setup as well. Test results reveal that careful construction of knowledge-base can increase the performance of NILM algorithms. MATLAB is used as the programming platform.

Smart meters are required to identify home appliances to fulfill various tasks in the smart grid environment. On the other hand, techniques using non-intrusive appliance load monitoring (NIALM) are yet to result in meaningful practical implementation. Our experimental setup, on the recommended specifications of the internal electrical wiring in Indian residences, used common household appliances' load signatures of active and reactive powers, harmonic components and their magnitudes. We have introduced a new approach of ‘multi point sensing’ and ‘group control’ rather than the ‘single point sensing’ and ‘individual control’, used so far in NIALM techniques. The disaggregation system based on Central Public Works Department of India (CPWD). One feature i.e. amplitude of first 8 odd harmonics of current signature of home appliances were selected for the classification. Further principle component analysis (PCA) is used. A comparison between these classification algorithms has been done and it is revealed that artificial neural network classifier and Bayes classifier provides 99.18% and 98.08% accuracy respectively for the experimental data.

Non-Intrusive Load Monitoring (NILM) disaggregates appliance-level energy consumption 
from aggregate load data, offering a practical approach for analyzing household energy use 
and promoting efficiency. Event detection, a critical element in NILM, identifies state 
transitions in appliances. However, existing approaches often struggle with real-world data, 
where challenges arise from subtle load changes, high-frequency noise, and load-switching 
transients, all of which impact detection accuracy. Additionally, high computational demands 
can limit real-time applicability and reduce adaptability to varied appliance behaviors. In this 
paper, we present a multi-window cumulative sum (CUSUM) method featuring adaptive 
thresholds and variable-length windows, enhanced by a dynamic parameter adjustment 
mechanism driven by signal statistics. To address noise interference, the method integrates 
median filtering and exponentially weighted moving average (EWMA) preprocessing. 
Dynamic parameter adjustments enable adaptation to fluctuating appliance behaviors, while 
multi-window accumulation and rate-of-change analysis improve discrimination between 
genuine events and spurious fluctuations. Experimental results demonstrate the proposed 
method's superiority over conventional CUSUM and other improved methods on PLAID, 
BLUED, and private datasets, achieving F1 scores of 94.72%, 96.43%, and 91.98%, 
respectively. Its robustness across thresholds, practicality, and low computational 
requirements highlight its potential for real-world NILM applications on resource-constrained 
devices.
Keywords: NILM, event detection, multi-window, adaptive threshold, variable-length

This paper presents a technical system, which uses the non-intrusive appliance load monitoring (NIALM) method. The solution enables the end user to observe the electricity consumption of the individual appliances. By dividing electricity consumption to individual appliances, it improves the users’ understanding of the overall energy use of the building. This way the owner of the building is able to intervene better with the energy consumption of the building. Observation of the electricity appliances is performed in a web application in a user interface calendar view.

Renewable energy sources are slowly but surely supplementing conventional sources of energy. These energy sources create a large number of micro-grids which become a part of the main grid. Smart meters are essential components in smart micro-grids and smart grids. Together they form a complex system which has become today an attractive area of research world-wide in view of enormous benefits of reliability and safety of power supply economically. Several studies in smart meter applications have shown that in residential buildings, energy consumption can be reduced up to 15% with better management. For this, a non-intrusive appliance load monitoring (NIALM) technique has been advocated which enables appliance identification to help better energy management. The identification is done using load signatures with appropriate algorithm. Several features of load signatures ranging from voltage and current profiles (steady states and transients) to a plethora of features such as active power (P) and reactive power (Q), power factor (pf), harmonic components (h), magnitudes of h (mh), current waveform (CW), eigen values, switching transient waveform, instantaneous admittance waveform, etc have been used in NIALM. The algorithms used vary from a `single algorithm' to `multiple algorithms', the latter used in `Committee Decision Mechanism' (CDM) in which majority of `votes' (i.e., agreement), `cast' by each of the algorithms, decides the final result of appliances identified. This scenario presents a serious difficulty with regard to choice of features and algorithms, besides the possibility of increasing the hardware and software costs of smart meters. The motivation for the study in this paper is provided by this scenario. The basic objective of this paper is to investigate the possibility of including only a limited number of features as load signature of appliances, and the use of only a single algorithm to effectively implement NIALM without any ambiguity. Results of our study so far indicate that generally only four features of P, Q, h and mh are sufficient for detection of appliances in operation; however, in some cases (~>10%), the additional feature, CW, may be needed for final identification.. The experiments conducted so far yielded unambiguous identification of appliances and the results are encouraging enough for possible use in practice for residential loads only.