Experimental determination of ZIP coefficients for residential appliances and ZIP model based appliance identification: The case of YTU Smart Home (original) (raw)
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IEEE Transactions on Power Delivery, 2014
This study presents the experimental determination of the ZIP (constant impedance (Z), constant current (I), constant power (P)) coefficients for residential appliances in a smart home at Yildiz Technical University (the YTU Smart Home) in Istanbul, Turkey. First, active and reactive power consumptions of house appliances are measured with respect to the voltage change between 100-240 V with a 10 V increment. Then, Least Square Algorithm is used to calculate ZIP coefficients of household appliances. Using the obtained ZIP coefficients and an assumed appliance usage pattern, a daily residential active and reactive power demand profiles are obtained. Data from the ZIP models are compared with actual measurement data. It is shown that the ZIP models are quite accurate within 200-220 V voltage range and can be used in generating residential demand profiles for system level analyses. In addition, the ZIP model based appliance identification algorithm is proposed to identify the plugged appliance in the smart home environment for better home energy management algorithm implementation.
The Determination of Load Profiles and Power Consumptions of Home Appliances
In recent years, the increment of distributed electricity generation based on renewable energy sources and improvement of communication technologies have caused the development of next-generation power grids known as smart grids. The structures of smart grids have bidirectional communication capability and enable the connection of energy generated from distributed sources to any point on the grid. They also support consumers in energy efficiency by creating opportunities for management of power consumption. The information on power consumption and load profiles of home appliances is essential to perform load management in the dwelling accurately. In this study, the power consumption data for all the basic home appliances, utilized in a two-person family in Çankırı, Turkey, was obtained with high resolution in one-second intervals. The detailed power consumption analysis and load profile were executed for each home appliance. The obtained data is not only the average power consumption of each appliance but also characterizes different operating modes or their cycles. In addition, the impact of these devices on home energy management studies and their standby power consumptions were also discussed. The acquired data is an important source to determine the load profile of individual home appliances precisely in home energy management studies. Although the results of this study do not completely reflect the energy consumption behavior of the people who live in this region, they can reveal the trends in load demands based on a real sample and customer consumption behavior of a typical two-person family.
IET Generation, Transmission & Distribution, 2018
Electrical load monitoring techniques are valuable to consumer site for energy saving, permitting reduction in electricity costs. Nowadays, smart grid technology incorporates advanced load monitoring applications, enabling efficient consumption of electrical energy. Non-intrusive load monitoring (NILM) is a moderately new practice to identify the power consumption of individual appliances of a consumer from the aggregated household at a single point of measurement. In this study, an improved NILM technique is proposed by using a shunt passive filter installed at the source side of any residential complex. The proposed method can be realised in two steps. The first step is to determine the harmonic impedance at the load side for different groups of loads for a single household. The second step is to implement a fuzzy rule-based approach for identification of different loads at the consumer end. Suitable simulations backed by experiments are demonstrated in this study to validate the viability of the proposed methodology.
Determination of zip coefficients for residential loads
Pressacademia
In this study, constant-impedance, constant-current and constant-power ZIP models have been analyzed for residental loads. Measurement based approach has been used to obtain ZIP models of selected loads. The measurement-based approach for load modeling is important because it reflects the real dynamics of the system and represents the load characteristics more accurately. The ZIP model is used to estimate the power drawn by the load depending on the voltage changes. Since the ZIP load model is a well-known model that provides power dependence in terms of the physical sense of voltage, studies are taking place in this area. In this study, the voltage and power values of selected residental loads at YTU Smart Home Laboratory are measured. Then, the ZIP coefficients have been calculated by using the least squares algorithm developed in MATLAB. The measured data is compared with the data from the obtained ZIP model for each appliance.
IEEE Access
The advent of information and communication technologies has paved the way for smart cities. Residential customers are the major consumers of electrical energy in such cities. Smart meters gather the energy consumption behavior of consumers at the aggregate/household level. Characterization of aggregate demand data has several advantages but significant benefits in terms of energy sustainability require Appliance Level Energy Characterization (ALEC). Various solutions for ALEC rely on sensors, smart plugs, smart appliances, smart meters, and/or energy disaggregation algorithms but smart meters with built-in energy disaggregation algorithms seem to be the most scalable option. This work is one of the pioneering contributions to present comprehensive applications and prospects of ALEC for smart residential communities. It also links these applications with 2050 decarbonization pathways and various United Nations (UN) sustainable development goals (SDGs). Prospective uses of ALEC in diverse fields such as power systems, health care, the social sciences, economics, surveillance, marketing, appliance manufacturing, technology development, etc. are highlighted. Moreover, the requirements and challenges hindering the large-scale deployment of the ALEC frameworks are outlined with some recommendations and open research directions. It is envisaged that ALEC of residential electricity can be exploited not only for achieving 2050 decarbonization targets but also for several 2030 SDGs. This work will provide a one-stop source of information on ALEC and will open the doors of cooperation among various stakeholders of smart cities to achieve long-term SDGs.
Energy management system and interactive functions of smart plug for smart home
MedCrave, Electrical & Electronic Technology Open Access Journal, 2018
Intelligent electronic equipment and automation network is the brain of high-technology energy management systems in the critical role of smart homes dominance. The smart home is a technology integration for greater comfort, autonomy, reduced cost, and energy saving as well. These services can be provided to homeowners for managing their home appliances locally or remotely and consequently allow them to automate intelligently and responsibly their consumption through an individual or collective control systems. In this study, some smart plugs with different features are analysed and one of them tested on typical household appliances. This article proposes to collect data with wireless technology with the aim to extract pertinent smart data for energy management system. This smart data allows quantifying the three kinds of load: intermittent load, phantom load and continuous load. Phantom load is a waste power that is one of the unnoticed power of each appliance while connected or disconnected to the main. Intermittent load and continuous load take into consideration the power and using time of home appliances. By using this classification, smart data will be defined to represent the different loads but also to reduce the communication of wireless sensor network in a smart home.
2012
This article describes a home energy management system that estimates household electricity consumption and costs. The system permits to build a home energy management system by connecting wirelessly smart appliances and smart meters. The system carries out electric measurement consumption, which enables to control the operation of smart appliances such as washing machines, refrigerators, electric stoves and other white goods. The system is based on an electronic module capable to communicate with the smart appliances integrated in the system, in order to obtain their consumption and control their operation according to the total household energy demand. Additionally, this module is capable to communicate with an electronic energy meter that supports two-ways communication (electrical utility and consumer) for the purpose of obtaining information regarding the total consumption, and receiving commands and notifications from the electricity supplier.
Usage monitoring of electrical devices in a smart home
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2011
Profiling the usage of electrical devices within a smart home can be used as a method for determining an occupant's activities of daily living. A nonintrusive load monitoring system monitors the electrical consumption at a single electrical source (e.g., main electric utility service entry) and the operating schedules of individual devices are determined by disaggregating the composite electrical consumption waveforms. An electrical device's load signature plays a key role in nonintrusive load monitoring systems. A load signature is the unique electrical behaviour of an individual device when it is in operation. This paper proposes a feature-based model, using the real power and reactive power as features for describing the load signatures of individual devices. Experimental results for single device recognition for 7 devices show that the proposed approach can achieve 100% classification accuracy with discriminant analysis using Mahalanobis distances.
An Experimental Study of Electrical Appliances Consumption Using Panel Meter Data
Journal of Power and Energy Engineering, 2017
The present study uses power data from panel meter connected to the micro-grid to identify electrical energy consumption of the school appliances and their behavior in both warm-up, standby and active operation states. Before the conduction of experiments a load auditing of the school appliances was carried out by reading the rated power of each device (e.g. photocopy machine, printer, and fridge). The captures of this kind of information were essential as it provides a starting point to determine energy use of each appliance and hence simplify the process of identification. The electric parameters such as active power, reactive power and current were used to analyze the behavior of electrical appliances in all states. Experimental results show that, both active and reactive power were found to be high for old Canon photocopy machine as compared to new Canon photocopy machine. Another experiment reveals that printing one copy by using HP laser printer consumes about 700 W, while photocopying one copy with new Canon machine utilizes approximately 1100 W. This study concludes that new photocopy machine consumes more electric energy in warm-up state as compared to other states (standby and active operation states). Future work is to develop an algorithm for demand side management strategies which will enable efficient utilization of the electric energy from the micro-grid and hence bring the intended energy impact to the school.
Individual Load Monitoring of Appliances for Home Energy Management System
International Journal of Electrical and Electronic Engineering & Telecommunications
Home energy management starts with a monitoring system for the user to become aware of how much energy he/she consumes over a period of time and a controlling system that maximizes energy efficiency. There are two methods of load monitoring used in analyzing loads in residential installations and one of them is Intrusive Load Monitoring (ILM). This study was aimed to create an energy management system focusing on individual load monitoring of household appliances through ILM implementation. Wireless network technology was also utilized for data transmission and access, using Raspberry Pi 3B+ and SenseTecnic cloud host. The notification feature of the system, done through a cloud-based communication platform Twilio, is 100% successful in performing its function. Energy consumption behavior model equations for specific types of appliance loads were generated using regression analysis. All equations have relatively good fit, with R squared of 85%-94%, and low standard error, except for the equation representing the variable load with sporadic consumption pattern. Nonetheless, there is 99% confidence in the accuracy of the energy consumption behavior. On the other hand, electric consumption of the entire smart meter costs PHP34.051 only for a month of operation. This only suggests that the system will not significantly contribute to the entire household electric energy consumption cost. Index Terms-Energy consumption behavior model, forecasting, intrusive load monitoring, regression analysis, predicted consumption I. INTRODUCTION Energy management usually consists of monitoring and controlling of energy in order to conserve it. It is one of the solutions to reduce the consumption of energy immediately and directly. It develops more due to the need for conservation of energy. Energy management is commonly applied to larger buildings such as industrial and commercial buildings, but recently, it started to be used in homes. Monitoring the energy consumption and collecting the data, analyzing the meter data to find the opportunity to reduce the energy waste, implementing the target opportunity to save energy and tracking if there is some progress in energy saving efforts are common steps in Manuscript