Performance Evaluation of Superstate HMM with Median Filter For Appliance Energy Disaggregation

Information on electricity consumption is one of the essential elements in terms of regulating the distribution of electricity in smart micro grid. Besides, information on electricity consumption can help consumers carry out an evaluation process to reduce electricity bill costs, which indirectly affect overall energy efficiency. One method in the process of monitoring electricity consumption is Non-Intrusive Load Monitoring (NILM). The main problem in NILM is to determine the energy disaggregation consumed by several equipment by merely performing the retrieval of data from only one measuring point. We used the Superstate Hidden Markov Model as the tool for modelling and analysis. A median data filter to the input data is applied to improve the performance of the disaggregation process. Based on the results of tests conducted using the REDD, the lowest accuracy was 96.69% for all tests performed

Nonintrusive Load Monitoring (NILM) Performance Evaluation A unified approach for accuracy reporting

Abstract Nonintrusive load monitoring (NILM), sometimes referred to as load disaggregation, is the process of determining what loads or appliances are running in a house from analysis of the power signal of the whole-house power meter. As the popularity of NILM grows, we find there is no consistent way researchers are measuring and reporting accuracies. In this short communication, we present a unified approach that would allow for consistent accuracy testing

A NON-INTRUSIVE IDENTIFICATION OF HOME APPLIANCES USING ACTIVE POWER AND HARMONIC CURRENT

In recent years, research on non-intrusive load monitoring has become very popular since it allows customers to better manage their energy use and reduce electrical consumption. The traditional non-intrusive load monitoring method, which uses active and reactive power as signatures, has poor performance in detecting small non-linear loads. This drawback has become more prominent because the use of nonlinear appliances has increased continuously during the last decades. To address this problem, we propose a NILM method that utilizes harmonic current in combination with the changes of real power. The advantages of the proposed method with respect to the existing frequency analysis based NILM methods are lower computational complexity and the use of only one feature to characterize the harmonic content of the current

Enabling Micro-level Demand-Side Grid Flexiblity in Resource Constrained Environments

The increased penetration of uncertain and variable renewable energy presents various resource and operational electric grid challenges. Micro-level (household and small commercial) demand-side grid flexibility could be a cost-effective strategy to integrate high penetrations of wind and solar energy, but literature and field deployments exploring the necessary information and communication technologies (ICTs) are scant. This paper presents an exploratory framework for enabling information driven grid flexibility through the Internet of Things (IoT), and a proof-of-concept wireless sensor gateway (FlexBox) to collect the necessary parameters for adequately monitoring and actuating the micro-level demand-side. In the summer of 2015, thirty sensor gateways were deployed in the city of Managua (Nicaragua) to develop a baseline for a near future small-scale demand response pilot implementation. FlexBox field data has begun shedding light on relationships between ambient temperature and load energy consumption, load and building envelope energy efficiency challenges, latency communication network challenges, and opportunities to engage existing demand-side user behavioral patterns. Information driven grid flexibility strategies present great opportunity to develop new technologies, system architectures, and implementation approaches that can easily scale across regions, incomes, and levels of development

Electricity consumption pattern disaggregation based on user utilization factor

Non-Intrusive Appliance Load Monitoring (NIALM) technique has been studied intensively by many researchers to estimate the electricity consumption of each appliance in a monitored building. However, the method requires a detailed, secondby- second power consumption data which is commonly not available without the use of high specification energy meter. The common energy meter used in buildings can only capture low frequency data such as kWh for every thirty minutes. This thesis proposes a bottom-up approach for disaggregating kWh consumption of a building. The relationship between the load profile of a building and electricity usage pattern of the occupants were studied and analysed. From the findings, a method based on utilization factor that relates user usage pattern and kWh electricity consumption was proposed to perform load disaggregation. The method was applied on the practical kWh profile data of electricity consumption of Block P19a, Fakulti Kejuruteraan Elektrik, Universiti Teknologi Malaysia. The disaggregated kWh consumption results for air-conditioning and lighting system were validated with the actual kWh consumption recorded at the respective branch circuits of the building. Results from the analysis showed that the proposed method can be used to disaggregate energy consumption of a commercial building into air-conditioning and lighting systems. The proposed method could be extended to disaggregate the energy consumption for different areas of the building

Identifying Appliances using NIALM with Minimum Features

Government of India has decided to install smart meters in fourteen states. Smart meters are required to identify home appliances to fulfill various tasks in the smart grid environment. Both intrusive and non-intrusive methods have been suggested for identification. However, intrusive method is not suitable for cost and privacy reasons. On the other hand, techniques using non-intrusive appliance load monitoring (NIALM) are yet to result in meaningful practical implementation. Two major challenges in NIALM research are the choice of features (load signatures of appliances), and the appropriate algorithm. Both have a direct impact on the cost of the smart meter. In this paper, we address the two issues and propose a procedure with only four features and a simple algorithm to identify appliances. 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 show that these four features are essential and sufficient for implementation of NIALM with a simple algorithm. 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.DOI:http://dx.doi.org/10.11591/ijece.v4i6.671

Detecting Activities of Daily Living and Routine Behaviours in Dementia Patients Living Alone Using Smart Meter Load Disaggregation

The emergence of an ageing population is a significant public health concern. This has led to an increase in the number of people living with progressive neurodegenerative disorders. The strain this places on services means providing 24-hour monitoring is not sustainable. No solution exists to non-intrusively monitor the wellbeing of patients with dementia, resulting in delayed intervention. Using machine learning and signal processing, domestic energy supplies can be disaggregated to detect appliance usage. This enables Activities of Daily Living (ADLs) to be assessed. The aim is to facilitate early intervention and enable patients to stay in their homes for longer. A Support Vector Machine (SVM) and Random Decision Forest classifier are modelled using data from three test homes. The trained models are then used to monitor two patients with dementia during a six-month clinical trial undertaken in partnership with Mersey Care NHS Foundation Trust. In the case of load disaggregation, the SVM achieved (AUC=0.86074, Sen=0.756 and Spec=0.92838). While the Decision Forest achieved (AUC=0.9429, Sen=0.9634 and Spec=0.9634). ADLs are also analysed to identify the behavioural patterns of the occupant while detecting alterations in routine. The approach is sensitive in identifying behavioural routines and detecting anomalies in patient behaviour

Charging load pattern extraction for residential electric vehicles: a training-free nonintrusive method

Extracting the charging load pattern of residential electric vehicle (REV) will help grid operators make informed decisions in terms of scheduling and demand-side response management. Due to the multistate and high-frequency characteristics of integrated residential appliances from the residential perspective, it is difficult to achieve accurate extraction of the charging load pattern. To deal with that, this article presents a novel charging load extraction method based on residential smart meter data to noninvasively extract REV charging load pattern. The proposed algorithm harnesses the low-frequency characteristics of the charging load pattern and applies a two-stage decomposition technique to extract the characteristics of the charging load. The two-stage decomposition technique mainly includes: the trend component of the charging load being decomposed by seasonal and trend decomposition using loess method, and the low-frequency approximate component being decomposed by discrete wavelet technology. Furthermore, based on the extracted characteristics, event monitoring, and dynamic time warping is applied to estimate the closest charging interval and amplitude. The key features of the proposed algorithm include 1) significant improvement in extraction accuracy; 2) strong noise immunity; 3) online implementation of extraction. Experiments based on ground truth data validate the superiority of the proposed method compared to the existing ones

A Review of Approaches for Sensing, Understanding, and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings

Buildings currently account for 30–40 percent of total global energy consumption. In particular, commercial buildings are responsible for about 12 percent of global energy use and 21 percent of the United States’ energy use, and the energy demand of this sector continues to grow faster than other sectors. This increasing rate therefore raises a critical concern about improving the energy performance of commercial buildings. Recently, researchers have investigated ways in which understanding and improving occupants’ energy-consuming behaviors could function as a cost-effective approach to decreasing commercial buildings’ energy demands. The objective of this paper is to present a detailed, up-to-date review of various algorithms, models, and techniques employed in the pursuit of understanding and improving occupants’ energy-use behaviors in commercial buildings. Previous related studies are introduced and three main approaches are identified: (1) monitoring occupant-specific energy consumption; (2) Simulating occupant energy consumption behavior; and (3) improving occupant energy consumption behavior. The first approach employs intrusive and non-intrusive load-monitoring techniques to estimate the energy use of individual occupants. The second approach models diverse characteristics related to occupants’ energy-consuming behaviors in order to assess and predict such characteristics’ impacts on the energy performance of commercial buildings; this approach mostly utilizes agent-based modeling techniques to simulate actions and interactions between occupants and their built environment. The third approach employs occupancy-focused interventions to change occupants’ energy-use characteristics. Based on the detailed review of each approach, critical issues and current gaps in knowledge in the existing literature are discussed, and directions for future research opportunities in this field are provided