Tatsuya Yamazaki

Bio: Tatsuya Yamazaki is an academic researcher from National Institute of Information and Communications Technology. The author has contributed to research in topics: Smart grid & Layer (electronics). The author has an hindex of 5, co-authored 31 publications receiving 295 citations.

Appliance Recognition from Electric Current Signals for Information-Energy Integrated Network in Home Environments

Abstract: We are developing a novel home network system based upon the integration of information and energy. The system aims to analyze user behavior with a power-sensing network and provide various life-support services to manage power and electric appliances according to user behavior and preferences. This paper describes an electric appliance recognition method using power-sensing data measured by CECU (Communication and Energy Care Unit) which is an intelligent outlet with voltage and current sensors to integrate legacy appliances (which are incompatible with a communications network) with the home network. Furthermore, we demonstrate a prototype home energy management system and examples of services based upon appliance recognition.

Determining location of appliances from multi-hop tree structures of power strip type smart meters

Abstract: Recently, energy management has become one of the emerging services in the area of residential network service. A smart meter is the most essential component of advanced metering infrastructure (AMI) that connects the home energy management system of individual residences and a smart grid that optimizes the production, distribution, and consumption of electric power. Power strip type smart meters can be used to not only monitor but also control the electric power consumption at individual power outlet ports in the power outlet directly. They can be used to control and effectively reduce standby power consumption by the application of the direct power supply control. A smart multi-power tap (SMPT) is an advanced multi-outlet power strip type smart meter that provides important contextual information such as the identity and location of electric home appliances on the basis of the temporal power consumption data and the control of power supply to the appliances. However, the SMPT cannot be used to determine the location of appliances when the connections among SMPTs form a tree structure. In this study, we develop a mathematical model of cascade connections among SMPTs and propose a solution for obtaining the location information of the tree structure. The proposed method helps realize real applications of the SMPT for providing activity-based context-aware home network services and energy management services.

AERO: extraction of user's activities from electric power consumption data

Abstract: Providing Feedback of power consumption by using Advanced Metering Infrastructure (AMI) and smart meters is considered the best method to lower consumers' power consumption. Automatic analysis of power consumption data that provides information on user activity is essential in order to provide effective feedback in time, and advanced services such as automatic power load control based on context information. The activity should be matched with user's behavior in order to make it understandable for the user and readable for the machine. In this paper, a new method for extracting activities from power consumption data is proposed. By using the concept of activities in daily living (ADLs), the method extracts the activities as tasks from context information such as identification and location of electric appliances and temporal power consumption from the AMI and the smart meters. Using ontology, the tasks are tagged by semantic meta-information in order to be used for sophisticated user friendly services.

Supply/demand adjustment system, supply/demand adjustment device, supply/demand adjustment method, and supply/demand adjustment program

Abstract: PROBLEM TO BE SOLVED: To provide a supply/demand adjustment system which can control power supply on the basis of the power consumption of a home electric appliance and a capacity at a power supply side, a supply/demand adjustment device, a supply/demand adjustment method, and a supply/demand adjustment program. SOLUTION: The supply/demand adjustment system 10 includes an adjustment server 12, and a power generation device 14 and an accumulation device 16 are connected to the adjustment server 12 so as to be communicable. The adjustment server 12 is connected to a plurality of home electric appliances 20 via a network 18 by power-line communication so as to be communicable. Furthermore, power from power sources such as a commercial power supply (32), the power generation device 14 and the accumulation device 16 is fed to the adjustment server 12 and each home electric appliance 20 via a power control device (30). When there is a power supply request from the home electric appliance 20, the adjustment server 12 determines whether the power supply is permitted or not on the basis of the required power of the home electric appliance 20 and the suppliable power of a power source, and notifies the home electric appliance 20 of the determination result. COPYRIGHT: (C)2010,JPO&INPIT

Simple and robust method for detecting the electrical appliances using markers and programmable logic devices

Abstract: The detection of appliances and their location is important for the efficient management and automatic operation of appliances and their power consumption. In this paper, we describe a simple and robust method of detecting appliances based on their location by using programmable logic devices (PLDs). The results of simulation and implementation are also included.

Smart Grid — The New and Improved Power Grid: A Survey

Abstract: The Smart Grid, regarded as the next generation power grid, uses two-way flows of electricity and information to create a widely distributed automated energy delivery network. In this article, we survey the literature till 2011 on the enabling technologies for the Smart Grid. We explore three major systems, namely the smart infrastructure system, the smart management system, and the smart protection system. We also propose possible future directions in each system. colorred{Specifically, for the smart infrastructure system, we explore the smart energy subsystem, the smart information subsystem, and the smart communication subsystem.} For the smart management system, we explore various management objectives, such as improving energy efficiency, profiling demand, maximizing utility, reducing cost, and controlling emission. We also explore various management methods to achieve these objectives. For the smart protection system, we explore various failure protection mechanisms which improve the reliability of the Smart Grid, and explore the security and privacy issues in the Smart Grid.

Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey

Abstract: Appliance Load Monitoring (ALM) is essential for energy management solutions, allowing them to obtain appliance-specific energy consumption statistics that can further be used to devise load scheduling strategies for optimal energy utilization. Fine-grained energy monitoring can be achieved by deploying smart power outlets on every device of interest; however it incurs extra hardware cost and installation complexity. Non-Intrusive Load Monitoring (NILM) is an attractive method for energy disaggregation, as it can discern devices from the aggregated data acquired from a single point of measurement. This paper provides a comprehensive overview of NILM system and its associated methods and techniques used for disaggregated energy sensing. We review the state-of-the art load signatures and disaggregation algorithms used for appliance recognition and highlight challenges and future research directions.

Unsupervised disaggregation of low frequency power measurements

Abstract: Fear of increasing prices and concern about climate change are motivating residential power conservation efforts. We investigate the effectiveness of several unsupervised disaggregation methods on low frequency power measurements collected in real homes. Specifically, we consider variants of the factorial hidden Markov model. Our results indicate that a conditional factorial hidden semi-Markov model, which integrates additional features related to when and how appliances are used in the home and more accurately represents the power use of individual appliances, outperforms the other unsupervised disaggregation methods. Our results show that unsupervised techniques can provide perappliance power usage information in a non-invasive manner, which is ideal for enabling power conservation efforts.