A systematic approach to ON-OFF event detection and clustering analysis of non-intrusive appliance load monitoring

The aim of non-intrusive appliance load monitoring (NIALM) is to disaggregate the energy consumption of individual electrical appliances from total power consumption utilizing non-intrusive methods. In this paper, a systematic approach to ON-OFF event detection and clustering analysis for NIALM were presented. From the aggregate power consumption data set, the data are passed through median filtering to reduce noise and prepared for the event detection algorithm. The event detection algorithm is to determine the switching of ON and OFF status of electrical appliances. The goodness-of-fit (GOF) methodology is the event detection algorithm implemented. After event detection, the events detected were paired into ON-OFF pairing appliances. The results from the ON-OFF pairing algorithm were further clustered in groups utilizing the K-means clustering analysis. The K-means clustering were implemented as an unsupervised learning methodology for the clustering analysis. The novelty of this paper is the determination of the time duration an electrical appliance is turned ON through combination of event detection, ON-OFF pairing and K-means clustering. The results of the algorithm implementation were discussed and ideas on future work were also proposed.     

A systematic approach in appliance disaggregation using k-nearest neighbours and naive Bayes classifiers for energy efficiency

One of the ways to achieve energy efficiency in various residential electrical appliances is with energy usage feedback. Research work done showed that with energy usage feedback, behavioural changes by consumers to reduce electricity consumption contribute significantly to energy efficiency in residential energy usage. In order to improve on the appliance-level energy usage feedback, appliance disaggregation or non-intrusive appliance load monitoring (NIALM) methodology is utilized. NIALM is a methodology used to disaggregate total power consumption into individual electrical appliance power usage. In this paper, the electrical signature features from the publicly available REDD data set are extracted by the combination of identifying the ON or OFF events of appliances and goodness-of-fit (GOF) event detection algorithm. The k-nearest neighbours (k-NN) and naive Bayes classifiers are deployed for appliances’ classification. It is observed that the size of the training sets effects classification accuracy of the classifiers. The novelty of this paper is a systematic approach of NIALM using few training examples with two generic classifiers (k-NN and naive Bayes) and one feature (power) with the combination of ON-OFF based approach and GOF technique for event detection. In this work, we demonstrated that the two trained classifiers are able to classify the individual electrical appliances with satisfactory accuracy level in order to improve on the feedback for energy efficiency.     

State identification of home appliance with transient features in residential buildings

Nonintrusive load monitoring (NILM) is crucial for extracting patterns of electricity consumption of household appliance that can guide users’ behavior in using electricity while their privacy is respected. This study proposes an online method based on the transient behavior of individual appliances as well as system steady-state characteristics to estimate the operating states of the appliances. It determines the number of states for each appliance using the density-based spatial clustering of applications with noise (DBSCAN) method and models the transition relationship among different states. The states of the working appliances are identified from aggregated power signals using the Kalman filtering method in the factorial hidden Markov model (FHMM). Thereafter, the identified states are confirmed by the verification of system states, which are the combination of the working states of individual appliances. The verification step involves comparing the total measured power consumption with the total estimated power consumption. The use of transient features can achieve fast state inference and it is suitable for online load disaggregation. The proposed method was tested on a high-resolution data set such as Labeled hIgh-Frequency daTaset for Electricity Disaggregation (LIFTED) and it outperformed other related methods in the literature.     

Internet of things and cloud computing‐based energy management system for demand side management in smart grid

A smart grid is an electricity network, which deals with electronic power conditioning and control of production, transmission, and distribution of electrical power by employing digital communication technologies to monitor and manage local changes in electricity usage. In the traditional power grid, energy consumers remain oblivious to their power consumption patterns, resulting in wasted energy as well as money. This issue is severely pronounced in the developing countries where there is a huge gap between demand and supply, resulting in frequent power outages and load‐shedding. For electrical energy savings, the smart grid employs demand side management (DSM), which refers to adaptation in consumer's demand for energy through various approaches such as financial incentives and awareness. The DSM in future smart grid must exploit automated energy management systems (EMS) built upon the state‐of‐the‐art technologies such as the internet of things (IoT) and cloud and/or fog computing. In this paper, we present the architecture framework, design, and implementation of an IoT and cloud computing‐based EMS, which generates load profile of consumer to be accessed remotely by utility company or by the consumer. The consumers' load profiles enable utility companies to regulate and disseminate their incentives and incite the consumers to adapt their energy consumption. Our designed EMS is implemented on a Project Circuit Board (PCB) to be easily installed at the consumer premises where it performs the following tasks: (a) monitors energy consumption of electrical appliances by means of our designed current and voltage sensors, (b) uploads sensed data to Google Firebase cloud over many‐to‐many IoT communication protocol Message Queuing Telemetry Transport (MQTT) where consumer's load profile is generated, which can be accessed via a web portal. These load profiles serve as input for implementing the various DSM approaches. Our results demonstrate generated load profiles of consumer load in terms of current, voltage, energy, and power accessible via a web portal.     

Decision-making in electrical appliance use in the home

This paper presents the results of a survey as well as an argument from the viewpoint of behavioral economics with the aim of clarifying how consumers make decisions about electrical appliance use in the home. A survey of consumers showed that most have little awareness of the energy efficiency of appliances, the price of the services produced by electrical appliances, or electricity rates. These findings indicate that price does not function as a signal in electricity consumption through electrical appliance use. Rather, we found that consumer decision-making in electricity consumption is dependent on the characteristics of the particular electrical appliances they use. Additionally, we argue that the payment system for home electricity consumption plays an important role in decision-making, causing biases due to aspects of human psychology discussed here in terms of satisficing and heuristics, payment decoupling, and budgeting. We conclude that decision-making about electrical appliance use and electricity consumption in the home is not always rational and is affected both by the particular characteristics of appliances and the payment system for electricity consumption along with human psychology.     

Energy labeling for electric fans in Malaysia

To reduce energy consumption in the residential sector, Malaysia Energy Commission is considering implementing energy labels for household electrical appliances including electric fans in 2005. The purpose of the energy labels is to provide the consumers a guideline to compare the size, features, price and efficiency of the appliance. This paper discusses the energy label for electric fans in this country based on Malaysian Standards developed by a technical committee that reviewed the performance of household electrical appliances. This study includes methodology for the calculation of the energy efficiency star rating and projected energy usage, performance requirements, details of the energy label and the requirements for the valid application in Malaysia. The label also can be adopted for other household electrical appliances with only slight modifications.     

Smart residential energy management system for demand response in buildings with energy storage devices

In the present scenario, the utilities are focusing on smart grid technologies to achieve reliable and profitable grid operation. Demand side management (DSM) is one of such smart grid technologies which motivate end users to actively participate in the electricity market by providing incentives. Consumers are expected to respond (demand response (DR)) in various ways to attain these benefits. Nowadays, residential consumers are interested in energy storage devices such as battery to reduce power consumption from the utility during peak intervals. In this paper, the use of a smart residential energy management system (SREMS) is demonstrated at the consumer premises to reduce the total electricity bill by optimally time scheduling the operation of household appliances. Further, the SREMS effectively utilizes the battery by scheduling the mode of operation of the battery (charging/floating/discharging) and the amount of power exchange from the battery while considering the variations in consumer demand and utility parameters such as electricity price and consumer consumption limit (CCL). The SREMS framework is implemented in Matlab and the case study results show significant yields for the end user.     

A method to generate synthetic hourly solar radiation globally

This paper develops a stochastic procedure for generating synthetic sets of hourly solar irradiation values, suitable for use in solar simulation design work. The daily atmospheric transmittance K_t for the day in question is broken down into hourly irradiation events by a stochastic disaggregation procedure. The necessary disaggregation models were constructed using the quasi-universal hourly atmospheric transmittance k_t, instead of the irradiation itself, as the random variable. Analyses of meteorological records revealed that both the marginal probability and stochastic features of sets of hourly events k_t within an individual day can be closely predicted using the K_t for the day. Results indicated that the parameters defining the disaggregation procedure were independent of geographical location. The values of k_t are found to be closely modeled by a Beta distribution, and a mean correlation coefficient between successive hourly values of k_t was found to be 0.54±0.14. An algorithm is described from which unlimited hourly solar irradiation data may, therefore, be generated using only the 12 monthly means of daily events, .     

Characterization of the electrical energy consumption of a building for the dimensioning of a solar‐hydrogen energy system

The photovoltaic (PV) applications where the dimensioning is effected through the daily energy balance criteria obtained by the estimation of the energy consumption depending on the power and time of use of the electrical apparatus are limited to autonomous PV systems with well‐defined end use. Applications where one would like to electrify complex end use, such as office buildings, schools, hospitals, laboratories, residential units, etc., quantifying the daily energy consumption is difficult mainly due to two aspects. First, there will be great number of a variety of electrical appliances and second the proportionate electrical consumption of each one of them is unpredictable. For this reason it is necessary to establish a methodology that permits one to quantify precisely the daily energy consumption pattern to predict the energetic functioning of the PV system whose size may be determined by this procedure. In this work we describe a methodology for the energetic quantification of the installed equipments by using a Power Quality Analyzer to obtain the historical global energy consumption, daily energy consumption (kWh day⁻¹, kVAh day⁻¹) and the energy quality for the dimensioning of the PV system. Copyright © 2009 John Wiley & Sons, Ltd.     

Energy efficiency and appliance purchases in Europe: Consumer profiles and choice determinants

This study seeks to the following: (1) understand the factors/characteristics typically considered when purchasing electrical appliances, (2) analyse the differences between consumer profiles regarding these and (3) understand the factors driving the consideration of energy efficiency class by purchasers. Results indicate a preference for first considering cost, followed by quality and energy consumption considerations. These are correlated positively with the consideration of energy efficiency class in consumer choices. Also, regression analysis shows environmental attitudes to be negative predictors of energy efficiency class consideration, while specific environmental behaviours were positive predictors. Finally, consumer profiles were identified based on gender, age and whether or not the purchaser was accompanied when decisions were made. Implications for retail employee training and the development of persuasive messages for consumers based on established profiles are discussed.     

Contribution to the analysis of urban residential electrical energy demand in developing countries

Access of large segments of the population to electrical energy continues to be limited in the cities of developing countries where the standards pertaining to installed power and consumption are ill suited to the level of appliances owned by households. In this paper, a new evaluation method of residential users’ actual needs in electricity is proposed for Cameroon. The approach is based on the determination of the nature, number and power of the appliances, as well as their operation periods. The method used is a survey administered to a representative sample of households. A detailed questionnaire appraises specific consumption and installed power and is also used to construct the households’ load curve. The need for electrical energy varies according to the size of the city and, inside the city, to household income. The average power used per dwelling accounts for less than half the value prescribed by current norms. If implemented, the method would help optimise network investments and operation. The load curve obtained in the experiment shows that the peak output power is recorded in the evening and that household demand makes up most of this charge. The proposed strategy would reduce electrical energy costs, thus being of great potential interest for poor urban populations.     

Analytical solution of a simulation model for wind turbines

It is shown that a recently introduced model for wind turbine dynamics, having the form J(dΩ/dt) = g(V,Ω) − T, admits of an analytical solution during periods of constant windspeed, V, when the load torque has the parabolic form T = KΩ². A certain function of the angular velocity turns out to be governed by a linear equation, characterised by a time constant. The special case of a model of Type 1 is shown to have a particularly simple solution.     

Methods to improve efficiency of four stroke, spark ignition engines at part load

The four stroke, spark ignition (SI) engine pressure–volume diagram (p–V) contains two main parts. They are the compression–combustion–expansion (high pressure loop) and the exhaust-intake (low pressure or gas exchange loop) parts. The main reason for efficiency decrease at part load conditions for these types of engines is the flow restriction at the cross sectional area of the intake system by partially closing the throttle valve, which leads to increased pumping losses and to increased low pressure loop area on the p–V diagram. Meanwhile, the poorer combustion quality, i.e. lower combustion speed and cycle to cycle variations, additionally influence these pressure loop areas. In this study, methods for increasing efficiency at part load conditions and their potential for practical use are investigated. The study also includes a review of the vast literature on the solution of this problem. This investigation shows that the potential for increasing the efficiency of SI engines at part load conditions is not yet exhausted. Each method has its own advantages and disadvantages. Among these, the most promising methods to decrease the fuel consumption at part load conditions are stratified charge and variable displacement engines. When used in combination, the other listed methods are more effective than their usage alone.     

Low power mode energy demand of household appliances—SELINA and APP projects

In the last decades, it has been recognized that energy consumption in low power modes for electrical and electronic products is an important issue. There is a need to expand energy efficiency efforts beyond simple standby modes into the new more complex area of networks, thus tackling the new paradigm of living based on the Internet of Things. The European project SELINA carried out a large scale in store monitoring campaign, measuring about 6300 different equipment. Since then, there is no reference to other similar market surveillance studies being carried out in Europe. In Asia, a market surveillance campaign performed by the Asia Pacific Partnership with measurements on a regular basis has been very successful. SELINA results show that 18.5% of the measured products present power values higher than the 2010 EC 1275/2008 regulation threshold in off-mode, and for standby this value reached 31%. When a comparison is made with the 2013 EC 1275/2008 regulation threshold, these values increase twice. The Asia Pacific Partnership results alert policy makers that low passive standby does not guarantee low active standby. Several studies indicate that consumer electronic products are becoming more efficient and their energy consumption is decreasing. However, because the ownership of appliances is also increasing, these improvements in energy efficiency do not seem to have significant impact in the overall consumption of the households. In addition, there is evidence that not all appliances in the market reach the performance announced by the manufacturers. Recent measurements carried out by the Natural Resources Defense Council on flat screen TVs revealed that their real energy consumption seems to be higher than announced in the label. This shows the urgent need for measurement campaigns, since no market surveillance is being carried out on regular basis, and trusting the manufacturer’s data seems to be unreliable.     

Read the label! Energy Star appliance label awareness and uptake among U.S. consumers

The Energy Star label program to promote the diffusion of energy efficient home appliances is arguably the most significant government effort to reduce U.S. residential energy consumption. Program effectiveness requires that consumers are aware of the labeling scheme and also change their purchase decisions based on label information. This paper examines the factors associated with consumer awareness of the Energy Star label of recently purchased ‘white’ major appliances and the factors associated with the choice of Energy Star labeled appliances. The paper finds that household characteristics have a much stronger association with consumer awareness of labels than with the choice of Energy Star appliances. Renting the home, Hispanic ethnicity, being poor or near poor, and living in regions with lower ACEEE scores do, however, decrease the propensity for households to purchase Energy Star appliances. Eliminating these gaps in Energy Star appliance adoption would result in house electricity cost savings of $164 million per year and associated carbon emission reductions of about 1.1 million metric tons per year.     

家电节能:一个时尚话题

与西方发达国家相比，我国家电能耗浪费是惊人的。西方发达国家早在上个世纪70年代就开始在家电降耗方面作出了卓有成效的努力。在我国，一场家电节能之战正在打响，冰箱和洗衣机的降耗尤其令商家重视。待机节能也是家电降耗的一个重要环节。     

Demand side management—A simulation of household behavior under variable prices

Within the next years, consumer households will be increasingly equipped with smart metering and intelligent appliances. These technologies are the basis for households to better monitor electricity consumption and to actively control loads in private homes. Demand side management (DSM) can be adopted to private households. We present a simulation model that generates household load profiles under flat tariffs and simulates changes in these profiles when households are equipped with smart appliances and face time-based electricity prices.     

What's driving energy efficient appliance label awareness and purchase propensity?

The EU appliance energy consumption labeling scheme is a key component of efforts to increase the diffusion of energy-efficient household appliances. In this paper, the determinants of consumer knowledge of the energy label for household appliances and the choice of class-A energy-efficient appliances are jointly estimated using data from a large survey of more than 20,000 German households. The results for five major appliances suggest that lack of knowledge of the energy label can generate considerable bias in both estimates of rates of uptake of class-A appliances and in estimates of the underlying determinants of choice of class-A appliance. Simulations of the choice to purchase a class-A appliance, given knowledge of the labeling framework, reveal that residence characteristics and, in several cases, regional electricity prices strongly increase the propensity to purchase a class-A appliance, but socio-economic characteristics have surprisingly little impact on appliance energy-class choice.     

基于电子电路拓扑结构模型驱动的非侵入式负荷识别

为区分相似负载之间的微妙差异,提出了一种家庭能源管理系统的非侵入式负荷识别方法,即利用模型驱动分类法的概念,重点研究了电源电路拓扑结构和多种常用电器的分类方法,前者是从电力电子角度提出分级分类方法,而后者则保证所提分类法在实际应用中的代表性。根据多层级分类方法,讨论了模型驱动分层特征提取的优点,结果表明该方法可获得90%以上的识别精度,从而为区分相似负载之间的微妙差异提供一种更简单、更可行的解决方案。     

A simple method for the determination of the optical constants, n and k of cadmium sulfide films from transmittance measurements

The dispersive refractive index n(λ) and extinction coefficient k(λ) of CdS thin films were evaluated from spectrophotometric transmittance characteristic in the visible region. A method is presented to determine n(λ) and k(λ) in the region k² n², using wavelength and transmittance values. This method has been used for the investigation of the effect of the deposition rate on the optical properties of cadmium sulfide films. The refractive indices are found to depend on the deposition rate with values between 2.48 and 2.60. The energy gap of the samples is found to be independent of deposition rate with the value 2.44 eV. The real and imaginary parts of the dielectric constant have also been evaluated.