Load Disaggregation Algorithm Research Articles

A Smart Socket for Real-Time Nonintrusive Load Monitoring

In the field of Non-Intrusive Load Monitoring (NILM), scientists across various institutions have published extensive research on both the hardware and the load disaggregation algorithm. However, there are few publicly available hardware design schemes for real-time load disaggregation. This increases the barrier to entry into the field. In this paper, a novel smart socket for load disaggregation by using a set of easily accessible sensors is presented, aiming to enable more researchers to actively participate in NILM quickly. In addition, we combine a time-efficient automatic state detection algorithm and a factorial hidden Markov model (ASD-FHMM) to achieve the goal of real-time load disaggregation. The proposed method also alleviates the problem of requiring extra information that broadly exists in the traditional Hidden Markov Model (HMM) based load disaggregation algorithms. Experiments on a public dataset as well as a dataset collected from our laboratory are conducted, and the results confirm the efficiency and effectiveness and of the proposed smart socket.

A Data-Centric Analysis of the Impact of Non-Electric Data on the Performance of Load Disaggregation Algorithms.

Recent research on non-intrusive load monitoring, or load disaggregation, suggests that the performance of algorithms can be affected by factors beyond energy data. In particular, by incorporating non-electric data in load disaggregation analysis, such as building and consumer characteristics, the estimation accuracy of consumption data may be improved. However, this association has rarely been explored in the literature. This work proposes a data-centric methodology for measuring the effect of non-electric characteristics on load disaggregation performance. A real-world dataset is considered for evaluating the proposed methodology, using various appliances and sample rates. The methodology results indicate that the non-electric characteristics may have varying effects on the performances of different building appliances. Therefore, the proposed methodology can be relevant for complementing load disaggregation analysis.

DeepEdge: A Novel Appliance Identification Edge Platform for Data Gathering, Capturing and Labeling.

With the development of the Internet of Things for smart grid, the requirement for appliance monitoring has become an important topic. The first and most important step in appliance monitoring is to identify the type of appliance. Most of the existing appliance identification platforms are cloud based, thus they consume large computing resources and memory. Therefore, it is necessary to explore an edge identification platform with a low cost. In this work, a novel appliance identification edge platform for data gathering, capturing and labeling is proposed. Experiments show that this platform can achieve an average appliance identification accuracy of 98.5% and improve the accuracy of non-intrusive load disaggregation algorithms.

Optimal Scheduling of Demand Response Aggregators in Industrial Parks Based on Load Disaggregation Algorithm

Nowadays, industrial parks play a significant role in the development of electricity market plans, and can thus provide excellent opportunities for market players to actively participate in various electricity markets. The demand response aggregator (DRA) is a major market player that can take advantage of these opportunities. In restructured electricity markets, identifying the consumption patterns of different classes of consumers can be effective in furthering the goals of the DRA. In previous studies on the self-scheduling of the DRA, consumer behavior has not been considered. Such an approach leads to numerous technical problems in the restructured electricity markets. For this purpose, herein, a practical mechanism is presented for executing the self-scheduling process of the DRA by considering the load disaggregation algorithm. The integration of self-scheduling and load disaggregation processes creates a hierarchical optimization problem. The main aim of the constructed hierarchical structure is to find the optimal self-scheduling of the DRA to consciously participate in the electricity markets by identifying the behavior of different consumers. The proposed structure is implemented and evaluated on the industrial park in Saveh, Iran. The time-of-use (TOU) and reward-based demand response (DR) programs are considered as the tools available for the DRA to trade the DR volumes in the day-ahead and balancing electricity markets.

Bats: An Appliance Safety Hazards Factors Detection Algorithm with an Improved Nonintrusive Load Disaggregation Method

In an electrical safe microenvironment, all kinds of electrical appliances can be operated safely to ensure the safety of life and property. The significance of safety hazard factors detection is to detect safety hazards in advance, to remind the administrators to exclude risk, to reduce the unnecessary loss, and to ensure that the electrical operation is healthy and orderly before the occurrence of accidents. In this paper, batteries are selected as the primary research subject of safety detection because batteries are used more and more in the Internet of Things (IOT), and they often cause fire in the process of discharging and charging. The existing algorithms need to be embedded into the specialized sensor for each important electrical appliance. However, they are limited by the actual deployment, so it is extremely difficult to spread widely. According to the opinions above, an improved load disaggregation algorithm based on dictionary learning and sparse coding with optimal dictionary matrix period is proposed to detect potential safety hazards of battery loads. For safety-related electrical applications, doing so can increase interpretability. Through experiments, we test this algorithm on the REDD dataset, and compare it with the baseline algorithms (combinatorial optimization, factorial hidden Markov model, basic discriminative dictionary sparse coding algorithm) to achieve a degree of trust. The Mean Absolute Error (MAE) value is 8.26, which drops by 70%. The Root Mean Square Error (RMSE) value is 97.75, which is also better than those baseline algorithms.

Development of a stochastic virtual smart meter data set for a residential building stock – methodology and sample data

Existing electricity smart meter data sets lack sufficient details on building parameters to evaluate the impact that home characteristics can have on electricity consumption. An extensive, open-source virtual smart meter (VSM) data set with corresponding building characteristics is provided. The methodology used to develop the VSM data is presented in detail. The data set consists of a variety of homes representative of a subset of the Canadian single-family home building stock. The building characteristics cover a wide range of values that are based on probability distributions developed using a segmentation and characterization process. The resulting framework and VSM data set can be used by researchers to develop classification models, verify load disaggregation algorithms, and for a variety of other purposes.

A Data-Driven Pivot-Point-Based Time-Series Feeder Load Disaggregation Method

The load profile at a feeder-head is usually known to utility engineers while the nodal load profiles are not. However, the nodal load profiles are increasingly important for conducting time-series analysis in distribution systems. Therefore, in this paper, we present a pivot-point based, two-stage feeder load disaggregation algorithm using smart meter data. The two stages are load profile selection (LPS) and load profile allocation (LPA). In the LPS stage, a random load profile selection process is first executed to meet the load diversity requirement. Then, a few pairs of pivot points are selected as the matching targets. After that, a matching algorithm will run repetitively to select one load profile at a time for matching the reference load profile at the pivot points. In the LPA stage, the LPS selected load profiles are allocated to each load node on the feeder considering distribution transformer loading limits, load composition, and square-footage. The proposed method is validated using actual data collected in a North Carolina service area. Simulation results show that the proposed method can generate a unique load shape for each load node while match the shape of their aggregated profile with the actual feeder head load profile.

An improved non-intrusive load disaggregation algorithm and its application

The non-intrusive load monitoring (NILM) method is proposed to acquire the energy consumption of appliances in a building. Steady-state current decomposition is one of the most effective and applicable methods in NILM. Although many load features and decomposing models have been developed in the previous work, the harmonic vectors have been rarely discussed. In this study, weighted current harmonic vectors are proposed to increase the weight of the useful harmonic. The harmonic vectors are calculated geometrically so that all the information in features can be retained. A multi-objective particle swarm optimization based model is built to carry out the disaggregation task, where both summation and standard deviation of the errors are considered as objective functions. Moreover, to make the public datasets available in current decomposition, a current superimposing method is proposed. It generates aggregated currents from the currents of appliances running independently. Finally, the proposed model and two contrast models are performed on the WHITED dataset. The experimental results indicate that the proposed method is more robust and has higher current disaggregation precision than the involved comparing methods.

Load Flexibility Forecast for DR Using Non-Intrusive Load Monitoring in the Residential Sector

Demand response services and energy communities are set to be vital in bringing citizens to the core of the energy transition. The success of load flexibility integration in the electricity market, provided by demand response services, will depend on a redesign or adaptation of the current regulatory framework, which so far only reaches large industrial electricity users. However, due to the high contribution of the residential sector to electricity consumption, there is huge potential when considering the aggregated load flexibility of this sector. Nevertheless, challenges remain in load flexibility estimation and attaining data integrity while respecting consumer privacy. This study presents a methodology to estimate such flexibility by integrating a non-intrusive load monitoring approach to load disaggregation algorithms in order to train a machine-learning model. We then apply a categorization of loads and develop flexibility criteria, targeting each load flexibility amplitude with a corresponding time. Two datasets, Residential Energy Disaggregation Dataset (REDD) and Refit, are used to simulate the flexibility for a specific household, applying it to a grid balancing event request. Two algorithms are used for load disaggregation, Combinatorial Optimization, and a Factorial Hidden Markov model, and the U.K. demand response Short Term Operating Reserve (STOR) program is used for market integration. Results show a maximum flexibility power of 200–245 W and 180–500 W for the REDD and Refit datasets, respectively. The accuracy metrics of the flexibility models are presented, and results are discussed considering market barriers.

A MultiScale Framework for Nonintrusive Load Identification

Nonintrusive load monitoring, i.e., the process of identifying individual load information from aggregate electrical measurements, is useful for a variety of smart grid applications including energy scorekeeping, condition monitoring, and activity tracking. Numerous load disaggregation algorithms have been used for nonintrusive monitoring. Many of these perform well only on certain datasets or load types, because transient electrical events can occur on vastly different time-scales and operating schedules with significantly different regularities. This paper presents a nonintrusive load monitoring framework that allows multiple algorithms to be used across multiple time-scales, with their outputs combined to enhance load recognition. Results are demonstrated with power system data from a United States Coast Guard Cutter (USCGC), demonstrating the utility of the framework for developing applications for condition-based maintenance, among other applications.

Building Virtual Power Meters for Online Load Tracking

Many energy optimizations require fine-grained, load-level energy data collected in real time, most typically by a plug-level energy meter. Online load tracking is the problem of monitoring an individual electrical load’s energy usage in software by analyzing the building’s aggregate smart meter data. Load tracking differs from the well-studied problem of load disaggregation in that it emphasizes per-load accuracy and efficient, online operation rather than accurate disaggregation of every building load via offline analysis. In essence, tracking a particular load creates a virtual power meter for it, which mimics having a networked-connected power meter attached to the load, but notably does not require tracking every other load as well. We propose PowerPlay , a model-driven system for performing accurate, high-performance online load tracking. Our results from applying the system to real-world energy data demonstrate that PowerPlay (i) enables efficient online tracking on low-power embedded platforms, (ii) scales to thousands of loads (across many buildings) on server platforms, and (iii) improves per-load accuracy by more than a factor of two compared to a state-of-the-art load disaggregation algorithm. Our results point to the potential of replacing physical energy meters by “virtual” power meters using a system like PowerPlay.

Energy Disaggregation of Appliances Consumptions Using HAM Approach

Non-intrusive load monitoring (NILM) makes it possible for users to track the energy consumption of a household. In this paper, we present a new hybrid energy disaggregation approach named HAM. This event-based load disaggregation algorithm uses an improved multi-layer Hungarian algorithm to match appliances transient features and a supervised adaptive resonance theory mapping neural network (ARTMAP) to cluster steady features. This approach using a modified dual sliding window-based cumulative sum control chart algorithm (DSWC) to detect the transient event first, and we convert the multi-dimensional electrical features of appliances into a bipartite graph matching problem. This way, an improved Hungarian algorithm is proposed to find the perfect matching when appliances are in different states. For classification and identification, the ARTMAP network is used to learn and classify the steady features of appliances. Furthermore, we introduced a grey correlation evaluation and multiple matching strategies to increase the fitness and accuracy of the approach. Experimental results demonstrate that the proposed HAM outperforms the comparative algorithm in both our resident appliances dataset (RAD) and BLUED public dataset. The proposed approach could also facilitate NILM more applicable for common households.

Exploiting HMM Sparsity to Perform Online Real-Time Nonintrusive Load Monitoring

Understanding how appliances in a house consume power is important when making intelligent and informed decisions about conserving energy. Appliances can turn ON and OFF either by the actions of occupants or by automatic sensing and actuation (e.g., thermostat). It is also difficult to understand how much a load consumes at any given operational state. Occupants could buy sensors that would help, but this comes at a high financial cost. Power utility companies around the world are now replacing old electro-mechanical meters with digital meters (smart meters) that have enhanced communication capabilities. These smart meters are essentially free sensors that offer an opportunity to use computation to infer what loads are running and how much each load is consuming (i.e., load disaggregation). We present a new load disaggregation algorithm that uses a super-state hidden Markov model and a new Viterbi algorithm variant which preserves dependencies between loads and can disaggregate multi-state loads, all while performing computationally efficient exact inference. Our sparse Viterbi algorithm can efficiently compute sparse matrices with a large number of super-states. Additionally, our disaggregator can run in real-time on an inexpensive embedded processor using low sampling rates.

Low Cost Disaggregation of Smart Meter Sensor Data

This paper proposes a novel load disaggregation algorithm, based on non-intrusive appliance monitoring that provides accurate results without the need of the reactive power component. The proposed technique requires the minimum possible number of sensor meters within the smart meter hardware and reduces the data traffic of the advanced metering infrastructure network, since it processes the only low-frequency smart meter data of the active power component of a residential unit. We propose a three-stage process for energy usage analysis, based on pulse extraction, pulse clustering and classification, and pulse to appliance association. The smart meter data are decomposed into a discrete set of pulses, and each pulse is associated with the operation of the appliances. The outcome of the algorithm is a pulse to appliance association that creates a daily load disaggregation map for the residential unit. In addition, this paper presents an overview and comparison of existing solutions that will help the reader obtain a good overview of the landscape of load disaggregation from smart meter sensor readings.

Real‐time load disaggregation algorithm using particle‐based distribution truncation with state occupancy model

A new particle-based distribution truncation method with a duration-dependent hidden semi-Markov model for non-intrusive appliance load monitoring is presented. Unlike earlier works, the approach keeps track of a set of states without prematurely pruning away intermediately ranked states. It also enables appliance state duration characteristics to be incorporated in a straightforward manner. Results show that the approach outperforms both the Viterbi algorithm and conventional particle-filtering methods.