Knowledge Representation for Event-Driven Metering

In electrical engineering, event-driven metering is a paradigm enabling different information coding with respect to the conventional time-domain metering techniques. It supplies unevenly spaced time series of compressed data representing energy measurements. This paper discusses about the data- and knowledge-representation of energy-based data in Low Voltage segments of Smart Grids. A representation based on tri-vectors and four-vectors is introduced to represent non-uniform linear time finite elements in unevenly spaced time series of metering data. The proposed representation is based on an original interpretation of process orientation in terms of accumulated energy. Practical examples taken from the data gathered from a new event-driven metering device are shown.

[1]  Zhu Han,et al.  Efficient and Secure Wireless Communications for Advanced Metering Infrastructure in Smart Grids , 2012, IEEE Transactions on Smart Grid.

[2]  Jian Liang,et al.  Load Signature Study—Part II: Disaggregation Framework, Simulation, and Applications , 2010, IEEE Transactions on Power Delivery.

[3]  Marina Papatriantafilou,et al.  Online and scalable data validation in advanced metering infrastructures , 2014, IEEE PES Innovative Smart Grid Technologies, Europe.

[4]  Qi Han,et al.  Circuit-Level Load Monitoring for Household Energy Management , 2011, IEEE Pervasive Computing.

[5]  John F. Sowa,et al.  Knowledge representation: logical, philosophical, and computational foundations , 2000 .

[6]  Jian Liang,et al.  Load Signature Study—Part I: Basic Concept, Structure, and Methodology , 2010, IEEE Transactions on Power Delivery.

[7]  Mikhail Simonov Coarse-grained cycle-accurate electricity metering , 2014, IEEE PES Innovative Smart Grid Technologies, Europe.

[8]  Olivier Terzo,et al.  Big Data Application: Analyzing Real-Time Electric Meter Data , 2013 .

[9]  G. W. Hart,et al.  Nonintrusive appliance load monitoring , 1992, Proc. IEEE.

[10]  Hsueh-Hsien Chang,et al.  A New Measurement Method for Power Signatures of Nonintrusive Demand Monitoring and Load Identification , 2011, IEEE Transactions on Industry Applications.

[11]  Alexander P. D. Mourelatos Events, processes, and states , 1978 .

[12]  James F. Allen Towards a General Theory of Action and Time , 1984, Artif. Intell..

[13]  Ivor W. Tsang,et al.  The Emerging "Big Dimensionality" , 2014, IEEE Computational Intelligence Magazine.

[14]  Mikhail Simonov Event-Driven Communication in Smart Grid , 2013, IEEE Communications Letters.

[15]  Federico Caleno,et al.  Design and deploy an innovative indoor device addressing the energy efficiency: A first step toward the smart box leveraging on the existing Enel AMM infrastructure , 2009 .

[16]  Hsueh-Hsien Chang,et al.  Particle-Swarm-Optimization-Based Nonintrusive Demand Monitoring and Load Identification in Smart Meters , 2012, IEEE Transactions on Industry Applications.

[17]  Michael Zeifman,et al.  Nonintrusive appliance load monitoring: Review and outlook , 2011, IEEE Transactions on Consumer Electronics.

[18]  Lingfeng Wang,et al.  Intelligent Multiagent Control System for Energy and Comfort Management in Smart and Sustainable Buildings , 2012, IEEE Transactions on Smart Grid.

[19]  Husheng Li,et al.  Gathering Process Data in Low-Voltage Systems by Enhanced Event-Driven Metering , 2017, IEEE Systems Journal.

[20]  Hsueh-Hsien Chang,et al.  Particle-Swarm-Optimization-Based Nonintrusive Demand Monitoring and Load Identification in Smart Meters , 2013 .

[21]  Mark Steedman,et al.  Temporal Ontology and Temporal Reference , 1988, CL.

[22]  Mikhail Simonov Hybrid Scheme of Electricity Metering in Smart Grid , 2014, IEEE Systems Journal.

[23]  R. Weron Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach , 2006 .

[24]  Walmir Freitas,et al.  An Event Window Based Load Monitoring Technique for Smart Meters , 2012, IEEE Transactions on Smart Grid.

[25]  Tassilo Pellegrin Economics of Big Data: A Value Perspective on State of the Art and Future Trends , 2013 .

[26]  A. Vojdani,et al.  Smart Integration , 2008, IEEE Power and Energy Magazine.