Appliance Models Research Articles

An Event-Based Nonintrusive Load Monitoring Approach: Using the Simplified Viterbi Algorithm

Nonintrusive load monitoring technologies are gaining popularity for their low energy monitoring costs. In the article, the authors present a simple event-detection algorithm based on maximum and minimum points. Then, they use a variant of hidden Markov model as the appliance model and combine it with event detection to reduce the input. Specifically, they propose a simplified Viterbi algorithm, which considers fewer state transitions each time than the traditional Viterbi. The experiment results show that their work can achieve higher than 90 percent accuracy for most high-power devices and 60–80 percent accuracy for most low-power devices. Meanwhile, the computational complexity could be much lower than with the traditional Viterbi algorithm.

Non-intrusive load monitoring by using active and reactive power in additive Factorial Hidden Markov Models

Non-intrusive load monitoring (NILM) is the task of determining the appliances individual contributions to the aggregate power consumption by using a set of electrical parameters measured at a single metering point. NILM allows to provide detailed consumption information to the users, that induces them to modify their habits towards a wiser use of the electrical energy. This paper proposes a NILM algorithm based on the joint use of active and reactive power in the Additive Factorial Hidden Markov Models framework. In particular, in the proposed approach, the appliance model is represented by a bivariate Hidden Markov Model whose emitted symbols are the joint active-reactive power signals. The disaggregation is performed by means of an alternative formulation of the Additive Factorial Approximate Maximum a Posteriori (AFAMAP) algorithm for dealing with the bivariate HMM models. The proposed solution has been compared to the original AFAMAP algorithm based on the active power only and to the seminal approach proposed by Hart (1992), based on finite state machine appliance models and which employs both the active and reactive power. Hart’s algorithm has been improved for handling the occurrence of multiple solutions by means of a Maximum A Posteriori technique (MAP). The experiments have been conducted on the AMPds dataset in noised and denoised conditions and the performance evaluated by using the F1-Measure and the normalized disaggregation metrics. In terms of F1-Measure, the results showed that the proposed approach outperforms AFAMAP, Hart’s algorithm, and Hart’s with MAP respectively by +14.9%, +21.8%, and +2.5% in the 6 appliances denoised case study. In the 6 appliances noised case study, the relative performance improvement is +25.5%, +51.1%, and +6.7%.

Poster abstract: Is the run-time of domestic appliances predictable?

Run-time forecasting or modeling of domestic appliances has gained more attention in recent years. Comparing currently published works though is difficult due to many differently employed datasets and performance measures. Our long-term aim is to facilitate future work by comparing known approaches on different datasets. Initial results from one method Barbato et al. (in: IEEE International Conference on Smart Grid Communications (SmartGridComm), pp 404–409, 2011) indicate that it is not suitable for the prediction of appliance usage.

Foresee: A user-centric home energy management system for energy efficiency and demand response

This paper presents foresee™, a user-centric home energy management system that can help optimize how a home operates to concurrently meet users’ needs, achieve energy efficiency and commensurate utility cost savings, and reliably deliver grid services based on utility signals. Foresee is built on a multiobjective model predictive control framework, wherein the objectives consist of energy cost, thermal comfort, user convenience, and carbon emission. Foresee learns user preferences on different objectives and acts on their behalf to operate building equipment, such as home appliances, photovoltaic systems, and battery storage. In this work, machine-learning algorithms were used to derive data-driven appliance models and usage patterns to predict the home’s future energy consumption. This approach enables highly accurate predictions of comfort needs, energy costs, environmental impacts, and grid service availability. Simulation studies were performed on field data from a residential building stock data set collected in the Pacific Northwest. Results indicated that foresee generated up to 7.6% whole-home energy savings without requiring substantial behavioral changes. When responding to demand response events, foresee was able to provide load forecasts upon receipt of event notifications and delivered the committed demand response services with 10% or fewer errors. Foresee fully utilized the potential of the battery storage and controllable building loads and delivered up to 7.0-kW load reduction and 13.5-kW load increase. These benefits are provided while maintaining the occupants’ thermal comfort or convenience in using their appliances.

The application of household appliances' flexibility by set of sequential uninterruptible energy phases model in the day-ahead planning of a residential microgrid

In this work, an accurate energy consumption model of household appliances based on Set of Sequential Uninterruptible Energy Phases (SSUEP) is applied to day-ahead energy management framework of a residential microgrid in order to effectively activate time-based demand response programs. The homes in the microgrid include the essential and/or shiftable household appliances accurately modeled by the SSUEP. These homes are also equipped with the photovoltaic systems, battery energy storages and electric vehicles. The residential microgrid is assumed to be connected to a smart grid such that bi-directional exchange of electric power would be possible. Being aware of the amount of power demand for the appliances and the day-ahead prices of the energy, the consumer provides the required energy from the photovoltaic systems, battery energy storages and electric vehicles (by Vehicle-to-Home and Vehicle-to-Grid capabilities). Moreover, using the flexibility of the shiftable loads, the consumer can be involved in the demand response strategies to reduce the costs. This flexibility is a result of delaying or anticipating the start time and the inter-phase delay modeled by the SSUEP. Lastly, the effects of the accurate SSUEP model on the day-ahead planning of the residential microgrid will be investigated by various scenarios.

Market penetration modeling of high energy efficiency appliances in the residential sector

The aim of this research is to model the market penetration of energy efficient appliances in the residential sector. The analysis focuses on six major appliances – refrigerators, freezers, clothes washers, clothes dryers, and ranges – to forecast their market penetration and market share during the years 2012–2050. Models were developed for each category using 22 years of historical data related to population, household income, immigration, and appliance price. These variables were selected based on the statistical tests of twelve macroeconomic variables. The market shares of high efficiency appliances were analyzed based on the related capital costs, operating costs, lifetime, and incentive. The results show that in Alberta the market penetration growth rate of dishwashers is higher than that of all other appliances, with a projected 30.52% increase between 2012 and 2050. The modeling results also indicate that the average annual energy consumption by refrigerators will decrease from 560.9 kWh in 2012 to 460.8 kWh in 2050, and this decrease indicates an annual energy efficiency improvement of 0.47%. In addition, the effect of an incentive on adopting high energy efficiency appliances and ultimately on energy efficiency improvement in Alberta is more effective for dishwashers and clothes washers.

Optimal energy flow control strategy for a residential energy local network combined with demand-side management and real-time pricing

Due to the increasing importance of demand-side resources in the energy internet, this paper proposes an optimal energy flow control strategy for a residential energy local network (RELN), which consists of a small number of households, based on the idea of demand-side management. In particular, a type of optimal and dynamic RELN energy consumption scheduling framework is formulated to minimize the daily total operation cost, while fully considering the output forecast error of renewable energy sources (RESs), the consumption preference of users and the status of the energy storage system. In this framework, we provide a unified appliance model to group various types of appliances, which are connected to the network, into a physical model with the same attributes, and a comprehensive real-time pricing mechanism between the RELN and power distribution company (PDC) is developed based on the generalized total load to guide the electricity consumption behavior of the end-users and to also balance the total residential load. On this basis, a mixed integer programming (MIP) model for dynamic energy management optimization of the RELN is optimized at each decision period and integrated into a model predictive control method to reduce the negative impacts of forecast errors of RESs, thereby realizing an optimal and automatic energy flow control for the RELN. Finally, the effectiveness of the proposed RELN energy flow management framework is verified using several case studies.

Optimal scheduling of household appliances for smart home energy management considering demand response

As an important part of demand-side management, residential demand response (DR) can not only reduce consumer’s electricity costs, but also improve the stability of power system operation. In this regard, this paper proposes an optimal scheduling model of household appliances for smart home energy management considering DR. The model includes electricity cost, incentive and inconvenience of consumers under time-of-use (TOU) electricity price. Further, this paper discusses the influence of inconvenience weighting factor on total costs. At the same time, the influence of incentive on optimization results is also analyzed. The simulation results show the effectiveness of the proposed model, which can reduce 34.71% of consumer’s total costs. It also illustrates that the total costs will be raised with the increase in inconvenience weighting factor. Thus, consumers will choose whether to participate in DR programs according to their preferences. Moreover, the result demonstrates that incentives are conducive to shifting load and reducing the consumer’s total energy costs. The presented study provides new insight for the applications of residential DR.

Finite State Machine Household's Appliances Models for Non-intrusive Energy Estimation

Non-intrusive loads monitoring NILM is a set of algorithms that aims to leverage smart meter data by extracting more useful information from the smart meter data. NILM involves disaggregation of individual household loads in term of their individual energy consumption. It is considered as low cost alternative to better understand the electrical network and reduce complexity of the management operations. It offers to households monitoring and control possibilities to their everyday energy consumption. This paper contributes toward non-intrusive energy estimation of household's loads through data-driven appliances modelling approach based on finite state machine models that mimic the real operations cycle. First, the models are built based on features extractions and events clustering via dynamic fuzzy clustering. The resulting clusters are further de-noised and processed to reveal accurate appliances operations states. Then finite state machine models are created using transition probability matrix and an optimization approach to extract the operation cycle that best describe real appliance operations. The evaluation of the framework was performed using two public datasets showing its performance to learn appliances models and energy estimation with an average error of 5% to 22%.

Multi-agent system to support decision-making process in design for recycling

In this paper, the application of multi-agent system to support decision-making process in design for recycling is presented. The design for recycling term is highlighted either as the design problem or from the point of view of regulations. The structure of agent system supporting the designer during the design process is showed. The basis of special kind of product model, that is the extension of standard 3D product model, called recycling-oriented product model, is described. The example results of analysis, based on real household appliance model, are presented.

Residential building energy demand and thermal comfort: Thermal dynamics of electrical appliances and their impact

Within a thermally well-insulated building, internal heat gains are getting more important on the point of the building thermal condition and its energy management [1–3]. In this study, we are focusing on thermal impact of heat dissipation of electrical appliances on buildings by applying a dynamic thermal model of electrical appliances. While a static model considers only power profile or power density of electrical appliances, the used dynamic model in this paper contains not only power profile but also thermal characteristics of electrical appliances. The thermal characteristics have been determined in previous study [4]. We shortly explain the methodology. The simulations are conducted with different values of thermal characteristics (power and RxC) representative of standard household appliances. The appliance thermal impact on the buildings in terms of heating energy demand and thermal comfort of occupants during a winter are investigated. This study is conducted within both a conventional residential building model and a thermally well-insulated residential building model. Two behaviors were identified: (i) below critical appliance power (600W, 200W for the B1 and B2, respectively), we observe fully power substitution of the heating energy demand. (ii) Above this critical power there is no fully substitution. Due to important temperature inhomogeneity within the different zones and the air renew flow structures coupled to the shift of appliance power consumption toward the unoccupied periods, the underlined lack of substitution is explained. It shows the necessity of dynamic thermal modeling of electrical appliances for more accurate and efficient building energy simulation and energy management especially for low energy buildings.

Real Time Information Based Energy Management Using Customer Preferences and Dynamic Pricing in Smart Homes

This paper presents real time information based energy management algorithms to reduce electricity cost and peak to average ratio (PAR) while preserving user comfort in a smart home. We categorize household appliances into thermostatically controlled (tc), user aware (ua), elastic (el), inelastic (iel) and regular (r) appliances/loads. An optimization problem is formulated to reduce electricity cost by determining the optimal use of household appliances. The operational schedules of these appliances are optimized in response to the electricity price signals and customer preferences to maximize electricity cost saving and user comfort while minimizing curtailed energy. Mathematical optimization models of tc appliances, i.e., air-conditioner and refrigerator, are proposed which are solved by using intelligent programmable communication thermostat ( iPCT). We add extra intelligence to conventional programmable communication thermostat (CPCT) by using genetic algorithm (GA) to control tc appliances under comfort constraints. The optimization models for ua, el, and iel appliances are solved subject to electricity cost minimization and PAR reduction. Considering user comfort, el appliances are considered where users can adjust appliance waiting time to increase or decrease their comfort level. Furthermore, energy demand of r appliances is fulfilled via local supply where the major objective is to reduce the fuel cost of various generators by proper scheduling. Simulation results show that the proposed algorithms efficiently schedule the energy demand of all types of appliances by considering identified constraints (i.e., PAR, variable prices, temperature, capacity limit and waiting time).

Residential Demand Response Scheduling With Multiclass Appliances in the Smart Grid

In this paper, we study an electricity load scheduling problem in a residence. Compared with previous works in which only limited sets of appliances are considered, we classify various appliances into five sets considering their different energy consumption and operation characteristics, and provide mathematical models for them. With these appliance models, we propose an electricity load scheduling algorithm that controls the operation time and energy consumption level of each appliance adapting to time-of-use pricing in order to maximize the overall net utility of the residence while satisfying its budget limit. The optimization problem is formulated as a mixed integer nonlinear programming (MINLP) problem, which is in general, difficult to solve. In order to solve the problem, we use the generalized Benders decomposition approach with which we can solve the MINLP problem easily with low computational complexity. By solving the problem, we provide an algorithm to obtain the optimal electricity load scheduling of various appliances with different energy consumption and operation characteristics in a unified way.

Load Monitoring Based on the Auxiliary Particle Filter Algorithm

<!--StartFragment --> This paper introduces family load monitoring based on auxiliary particle filter algorithm. It mainly uses a set of random samples with relevant weights to estimate the posterior probability density p(x_t|Y^t). First of all, the model of household electrical appliances is established in this paper, then using the particle filter algorithm to estimate the state. It mainly consists of two parts: including Bayesian estimation and auxiliary particle filter-based load monitoring. Finally, the data collected by the sensor is simulated on the MATLAB platform, and the simulation results are obtained by using the evolutionary auxiliary particle filter algorithm.

MinNet: toward more intelligent smart home energy management systems with fewer sensors

The smart home technology has attracted considerable attention for decades. Nowadays, as the drastic increase of home energy consumption, efficient home energy management system HEMS has become a major building block. Aiming at building an easy-to-deploy intelligent HEMS, we design MinNet to i monitor the states of individual home appliances using the minimum number of power sensors and ii provide occupancy states of a house/room with inference intelligence. In detail, we use convenient plug-in power sensors to construct a wireless sensor network for data collection and establish an analytical framework based on the power model of appliances. By solving a carefully-designed optimisation problem, we find the minimum number of sensors to decode the states of appliances from their aggregated power and infer the occupancy states of the house/room. We implement MinNet in a house and validate its accuracy in appliance states monitoring and occupancy inference.

On a Training-Less Solution for Non-Intrusive Appliance Load Monitoring Using Graph Signal Processing

With ongoing large-scale smart energy metering deployments worldwide, disaggregation of a household’s total energy consumption down to individual appliances using analytical tools, also known as non-intrusive appliance load monitoring (NALM), has generated increased research interest lately. NALM can deepen energy feedback, support appliance retrofit advice, and support home automation. However, despite the fact that NALM was proposed over 30 years ago, there are still many open challenges with respect to its practicality and effectiveness at low sampling rates. Indeed, the majority of NALM approaches, supervised or unsupervised, require training to build appliance models, and are sensitive to appliance changes in the house, thus requiring regular re-training. In this paper, we tackle this challenge by proposing an NALM approach that does not require any training. The main idea is to build upon the emerging field of graph signal processing to perform adaptive thresholding, signal clustering, and pattern matching. We determine the performance limits of our approach and demonstrate its usefulness in practice. Using two open access datasets—the US REDD data set with active power measurements downsampled to 1 min resolution and the UK REFIT data set with 8-s resolution, we demonstrate the effectiveness of the proposed method for typical smart meter sampling rate, with the state-of-the-art supervised and unsupervised NALM approaches as benchmarks. 1 1 Part of this work was presented at IEEE GlobalSIP-2015 [1] . The REFIT dataset used to generate the results can be accessed via DOI 10.15129/31da3ece-f902-4e95-a093-e0a9536983c4.

A three-dimensional finite element analysis of molar distalization with a palatal plate, pendulum, and headgear according to molar eruption stage.

ObjectiveThis study aimed to (1) evaluate the effects of maxillary second and third molar eruption status on the distalization of first molars with a modified palatal anchorage plate (MPAP), and (2) compare the results to the outcomes of the use of a pendulum and that of a headgear using three-dimensional finite element analysis.MethodsThree eruption stages were established: an erupting second molar at the cervical one-third of the first molar root (Stage 1), a fully erupted second molar (Stage 2), and an erupting third molar at the cervical one-third of the second molar root (Stage 3). Retraction forces were applied via three anchorage appliance models: an MPAP with bracket and archwire, a bone-anchored pendulum appliance, and cervical-pull headgear.ResultsAn MPAP showed greater root movement of the first molar than crown movement, and this was more noticeable in Stages 2 and 3. With the other devices, the first molar showed distal tipping. Transversely, the first molar had mesial-out rotation with headgear and mesial-in rotation with the other devices. Vertically, the first molar was intruded with an MPAP, and extruded with the other appliances.ConclusionsThe second molar eruption stage had an effect on molar distalization, but the third molar follicle had no effect. The application of an MPAP may be an effective treatment option for maxillary molar distalization.

Electricity Consumption Constraints for Smart-home Automation: An Overview of Models and Applications

The global energy consumption challenge can largely be addressed by adoption of smart-home autonomous agents, allowing adaptive scheduling of electronic devices – for household and businesses alike. The realization of this smart architecture necessitates the agents need to know their decision space, in order to schedule different devices according to their individual constraints. Some of these constraints can be set by the owner, for example, timelines for washing machine or charging electric vehicles, not necessitating a smart agent. However, most appliances are subject to physical constraints, for example, washing machine being non-interruptible, and consequently, the modeling of the decision space is non-trivial. This paper gives an overview of smart-agent based models for different electric devices and appliances using their individual non-trivial energy consumption constraints, and proposes solutions for appliance optimization and scheduling.

Energy consumption of cold climate heat pumps in different climates – Comparison of single-stage and two-stage systems

The aim of the study presented in this paper is the comparison of different possibilities to reduce the power consumption and environmental impact of barrack buildings of the US army. For this purpose different building standards, different heating and cooling appliances and a cold storage concept are compared using annual building simulations. These simulations are carried out for central and northern locations in the US. The focus of this paper is the applicability of cold climate heat pumps in these climates. A coupled building and appliance model has been established to allow a fair comparison based on first energy usage and CO2 emissions. Altogether seven different building configurations in six different locations have been investigated. Simulation results show the most significant energy savings (up to 90%) can be achieved by improving building insulation and heat recovery of the ventilation system. Second major improvement is the usage of two-stage heat pump systems, which drastically reduces the need for backup heating systems and lead to seasonal efficiencies (SCOPs) in the area of 3.0, or in other words an energy saving of 20–35%. Thermal storage and control concepts such as night setback in comparison show little effect. The simulation results also showed that primary energy demand or greenhouse gas emissions are better measures for the comparison of environmental impact than COP and SCOP.

Thermal network of coupled building and electrical appliances

As power density of electrical appliances in a building sector grows up, its thermal effect also increases especially within a well-insulated building. In order to investigate the heat gain of these appliances, this paper proposes a dynamic thermal model of electrical appliances which is embedded in a building thermal analysis by using thermal network method. For this, thermal-electrical analogy and a static model and a dynamic model of electrical appliance coupled to a thermal network of a building are presented. As applications, a monitor and a computer are selected and their thermal models are adapted to a building model. Finally, their thermal influence is observed by experimental results and simulation one.