The Experts below are selected from a list of 12003 Experts worldwide ranked by ideXlab platform

Shiyan Hu - One of the best experts on this subject based on the ideXlab platform.

  • Uncertainty-Aware Household Appliance Scheduling Considering Dynamic Electricity Pricing in Smart Home
    IEEE Transactions on Smart Grid, 2013
    Co-Authors: Xiaodao Chen, Shiyan Hu
    Abstract:

    High quality demand side management has become indispensable in the smart grid infrastructure for enhanced energy reduction and system control. In this paper, a new demand side management technique, namely, a new energy efficient scheduling algorithm, is proposed to arrange the Household Appliances for operation such that the monetary expense of a customer is minimized based on the time-varying pricing model. The proposed algorithm takes into account the uncertainties in Household Appliance operation time and intermittent renewable generation. Moreover, it considers the variable frequency drive and capacity-limited energy storage. Our technique first uses the linear programming to efficiently compute a deterministic scheduling solution without considering uncertainties. To handle the uncertainties in Household Appliance operation time and energy consumption, a stochastic scheduling technique, which involves an energy consumption adaptation variable , is used to model the stochastic energy consumption patterns for various Household Appliances. To handle the intermittent behavior of the energy generated from the renewable resources, the offline static operation schedule is adapted to the runtime dynamic scheduling considering variations in renewable energy. The simulation results demonstrate the effectiveness of our approach. Compared to a traditional scheduling scheme which models typical Household Appliance operations in the traditional home scenario, the proposed deterministic linear programming based scheduling scheme achieves up to 45% monetary expense reduction, and the proposed stochastic design scheme achieves up to 41% monetary expense reduction. Compared to a worst case design where an Appliance is assumed to consume the maximum amount of energy, the proposed stochastic design which considers the stochastic energy consumption patterns achieves up to 24% monetary expense reduction without violating the target trip rate of 0.5%. Furthermore, the proposed energy consumption scheduling algorithm can always generate the scheduling solution within 10 seconds, which is fast enough for Household Appliance applications.

Barry P. Hayes - One of the best experts on this subject based on the ideXlab platform.

  • Load Identification and Classification of Activities of Daily Living using Residential Smart Meter Data
    2019 IEEE Milan PowerTech, 2019
    Co-Authors: Michael A. Devlin, Barry P. Hayes
    Abstract:

    This paper develops an approach for Household Appliance identification and classification of Household Activities of Daily Living (ADLs) using residential smart meter data. The process of Household Appliance identification, i.e. decomposing a mains electricity measurement into each of its constituent individual Appliances, is a very challenging classification problem. Recent advances have made deep learning a dominant approach for classification in fields such as image processing and speech recognition. This paper presents a deep learning approach based on multi-layer, feedforward neural networks that can identify common Household electrical Appliances from a typical Household smart meter measurement. The performance of this approach is tested and validated using publicly-available smart meter data sets. The identified Appliances are then mapped to Household activities, or ADLs. The resulting ADL classifier can provide insights into the behaviour of the Household occupants, which has a number of applications in the energy domain and in other fields.

  • Non-Intrusive Load Monitoring and Classification of Activities of Daily Living Using Residential Smart Meter Data
    IEEE Transactions on Consumer Electronics, 2019
    Co-Authors: Michael A. Devlin, Barry P. Hayes
    Abstract:

    This paper develops an approach for Household Appliance identification and classification of Household activities of daily living (ADLs) using residential smart meter data. The process of Household Appliance identification, i.e., decomposing a mains electricity measurement into each of its constituent individual Appliances, is a very challenging classification problem. Recent advances have made deep learning a dominant approach for classification in fields, such as image processing and speech recognition. This paper presents a deep learning approach based on multilayer, feedforward neural networks that can identify common Household electrical Appliances from a typical Household smart meter measurement. The performance of this approach is tested and validated using publicly available smart meter data sets. The identified Appliances are then mapped to Household activities, or ADLs. The resulting ADL classifier can provide insights into the behavior of the Household occupants, which has a number of applications in the energy domain and in other fields.

Matthias Bucher - One of the best experts on this subject based on the ideXlab platform.

  • A dynamic Household Appliance stock model for load management introduction strategies
    2011 8th International Conference on the European Energy Market (EEM), 2011
    Co-Authors: Matthias Bucher, Stephan Koch, Göran Andersson
    Abstract:

    In this paper, the dynamics of market introduction of new electric Household Appliances and their gradual propagation into the existing Appliance stock is investigated. The study is focussed on the release of Appliances that possess a certain new property, in the present case a communication interface for “Smart Grid” applications such as sophisticated load management methods. Understanding the introduction dynamics is particularly relevant for estimating the amount of installed load management compatible Household Appliances and the available control potential in a given year in the future, which is essential for “Smart Grid” business model development. Due to the relatively long life span of Household Appliances, it can be shown that it takes up to several decades to achieve a complete replacement of the conventional Appliance stock in the absence of additional measures. The focus of the paper is on methodology and application of stock models describing the number of Appliances “in the field” over time. Different approaches depending on the availability of input data are illustrated and the effect of additional measures, such as replacement incentives, is evaluated. Results are given for the Swiss market of refrigerators, freezers, and heat pumps.

  • Dynamic Modelling of Household Appliance Stocks for Assessing Load Management Introduction Strategies
    ETH Zürich, 2010
    Co-Authors: Matthias Bucher
    Abstract:

    A part of the research eld of Load Management in electric power systems addresses thermostatically controlled Household Appliances, which shall be connected to the communication network of the electricity utility in order to contribute with their thermal storage capacity to control tasks of the electrical energy system. Some Appliances can be retro tted easily, while others require an integration of the Load Management communication de- vice into the Appliance and have to be redesigned. This report depicts how to model the future stock of Load Management compatible Appliances and their Load Management potential. The results of these models shall provide a basis for Load Management introduction strategies and new business mod- els. A vintage model is introduced to model the evolution of the Appliance stocks. The calculations of the Load Management potential are based on the assumption that the dynamics of the Appliances' energy contents behave like a rst order system. Along with the explanations of the methodology of modelling stock and potential, an implementation of these models in a MATLAB-based program is illustrated

Yoshinosuke Arai - One of the best experts on this subject based on the ideXlab platform.

  • Study on coordination between SPD in panel-board and SPD-component of an electric Household Appliance
    2011 7th Asia-Pacific International Conference on Lightning, 2011
    Co-Authors: Yasuhiro Miyama, Shunichi Yanagawa, Takashi Sawamura, Yoshinosuke Arai
    Abstract:

    The authors have investigated the coordination between the SPD in a panel-board and the SPD components of an electric Household Appliance. Study results clarified the following items. Installing a SPD in a panel-board can reduce the capacity of SPDC (surge protective device components) in an electric Household Appliance. The SPD in a panel-board is better to be grounded at the panel-board side for the flow of surge current, than at Appliance side. When it is grounded at the electrical Household Appliances side, the surge current value of SPD and SPDC is decided by the operating voltage of MOV (SPD and SPDC).

  • Experimental study on Coordination between SPD in panel-board and SPD-component of an electric Household Appliance
    2010 Asia-Pacific International Symposium on Electromagnetic Compatibility, 2010
    Co-Authors: Yasuhiro Miyama, Shunichi Yanagawa, Takashi Sawamura, Yoshinosuke Arai
    Abstract:

    The authors have investigated the coordination between the SPD in a panel-board and the SPD components of an electric Household Appliance. Experimental results clarified the following items. Installing a SPD in a panel-board can reduce the capacity of SPDC(surge protective device components) in an electric Household Appliance. Depending upon the position of a grounding terminal of the SPD in a panel-board, the SPDC in an electric Household Appliance may operate. The V-I characteristics of the SPDs in the panel-board and the SPD components have the important roles for deciding the peak value of flowing current and the waveforms of it. And we can control the flowing current of SPDC in an electric Household Appliance by adjusting the V-I characteristics of the SPD in a panel-board.

  • Experimental study on Coordination between SPD in panel-board and SPD-component of an electric Household Appliance
    2010 Asia-Pacific International Symposium on Electromagnetic Compatibility, 2010
    Co-Authors: Yasuhiro Miyama, Shunichi Yanagawa, Takashi Sawamura, Yoshinosuke Arai
    Abstract:

    The authors have investigated the coordination between the SPD in a panel-board and the SPD components of an electric Household Appliance. Experimental results clarified the following items.

Yu-chen Kuo - One of the best experts on this subject based on the ideXlab platform.

  • Design and Implementation of a Smart Home System Using Multisensor Data Fusion Technology
    Sensors, 2017
    Co-Authors: Yu-liang Hsu, Hsing Cheng Chang, Shyan Lung Lin, Shih Chin Yang, Chih-chien Chang, Yuan Sheng Cheng, Heng-yi Su, Po-huan Chou, Yu-chen Kuo
    Abstract:

    This paper aims to develop a multisensor data fusion technology-based smart home system by integrating wearable intelligent technology, artificial intelligence, and sensor fusion technology. We have developed the following three systems to create an intelligent smart home environment: (1) a wearable motion sensing device to be placed on residents’ wrists and its corresponding 3D gesture recognition algorithm to implement a convenient automated Household Appliance control system; (2) a wearable motion sensing device mounted on a resident’s feet and its indoor positioning algorithm to realize an effective indoor pedestrian navigation system for smart energy management; (3) a multisensor circuit module and an intelligent fire detection and alarm algorithm to realize a home safety and fire detection system. In addition, an intelligent monitoring interface is developed to provide in real-time information about the smart home system, such as environmental temperatures, CO concentrations, communicative environmental alarms, Household Appliance status, human motion signals, and the results of gesture recognition and indoor positioning. Furthermore, an experimental testbed for validating the effectiveness and feasibility of the smart home system was built and verified experimentally. The results showed that the 3D gesture recognition algorithm could achieve recognition rates for automated Household Appliance control of 92.0%, 94.8%, 95.3%, and 87.7% by the 2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, and leave-one-subject-out cross-validation strategies. For indoor positioning and smart energy management, the distance accuracy and positioning accuracy were around 0.22% and 3.36% of the total traveled distance in the indoor environment. For home safety and fire detection, the classification rate achieved 98.81% accuracy for determining the conditions of the indoor living environment.