A Semantic Web Based Approach to Knowledge Management for Grid Applications

Knowledge has become increasingly important to support intelligent process automation and collaborative problem solving in large-scale science over the Internet. This paper addresses distributed knowledge management, its approach and methodology, in the context of grid application. We start by analyzing the nature of grid computing and its requirements for knowledge support; then, we discuss knowledge characteristics and the challenges for knowledge management on the grid. A semantic Web-based approach is proposed to tackle the six challenges of the knowledge lifecycle - namely, those of acquiring, modeling, retrieving, reusing, publishing, and maintaining knowledge. To facilitate the application of the approach, a systematic methodology is conceived and designed to provide a general implementation guideline. We use a real-world Grid application, the GEODISE project, as a case study in which the core semantic Web technologies such as ontologies, semantic enrichment, and semantic reasoning are used for knowledge engineering and management. The case study has been fully implemented and deployed through which the evaluation and validation for the approach and methodology have been performe

HOMEBOTS: Intelligent Decentralized Services for Energy Management

The deregulation of the European energy market, combined with emerging advanced capabilities of information technology, provides strategic opportunities for new knowledge-oriented services on the power grid. HOMEBOTS is the namewe have coined for one of these innovative services: decentralized power load management at the customer side, automatically carried out by a `society' of interactive household, industrial and utility equipment. They act as independent intelligent agents that communicate and negotiate in a computational market economy. The knowledge and competence aspects of this application are discussed, using an improved \ud version of task analysis according to the COMMONKADS knowledge methodology. Illustrated by simulation results, we indicate how customer knowledge can be mobilized to achieve joint goals of cost and energy savings. General implications for knowledge creation and its management are discussed

Methods and tools for Knowledge Management in research centres

International audienceIn the Knowledge Based Economy, research centres whether industrial or public, play a fundamental role. In terms of Knowledge Management, these organisations have a special status, because their production is knowledge and only knowledge. The Knowledge Capital they accumulate in their activities therefore is a strong strategic issue and the management of these assets has become crucial. The problem addressed in this paper is to design a pertinent methodology for Knowledge Management considering the specificity of knowledge production by research centres. This methodology is based on a suitable model to describe that knowledge production. The reference model is built on knowledge flows between the organisation and its knowledge workers, and a subsystem called “Knowledge Capital”. A research centre is defined by the fact that its product is only knowledge and is accumulated in its knowledge subsystem. Some economical characteristics of this Knowledge Capital are shown as being very adapted to knowledge produced in research centres. The methodology is based on two tools. The first tool is the knowledge map that can represent a comprehensive model of the Knowledge Capital of the organisation, which is often not well known or unstructured. That map is built on a shared and consensual vision of the main knowledge actors. It is not a map produced by a knowledge tool, but a co‑construction (through interviews) with the knowledge actors. The second tool is a grid for criticality analysis (Critical Knowledge Factors), which evaluates the knowledge domains of the organisation and suggests appropriate actions to be put in place for the most critical domains. This tool is a guide for interviewing knowledgeable actors in the organisation, to collect and analyse a set of data for decision support. The aim of the methodology is to provide a set of recommendations to build a KM plan of actions to preserve, share and make evolve the Knowledge Capital. The methodology has been elaborated through constant feed‑back with practice, and has been validated in many real cases in various countries. Three case studies (France, Brazil, and Canada) are succinctly described to exemplify the effectiveness of the methodology

Novel processes for smart grid information exchange and knowledge representation using the IEC common information model

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The IEC Common Information Model (CIM) is of central importance in enabling smart grid interoperability. Its continual development aims to meet the needs of the smart grid for semantic understanding and knowledge representation for a widening domain of resources and processes. With smart grid evolution the importance of information and data management has become an increasingly pressing issue not only because far more data is being generated using modern sensing, control and measuring devices but also because information is now becoming recognised as the ‘integral component’ that facilitates the optimal flexibility required of the smart grid. This thesis looks at the impacts of CIM implementation upon the landscape of smart grid issues and presents research from within National Grid contributing to three key areas in support of further CIM deployment. Taking the issue of Enterprise Information Management first, an information management framework is presented for CIM deployment at National Grid. Following this the development and demonstration of a novel secure cloud computing platform to handle such information is described. Power system application (PSA) models of the grid are partial knowledge representations of a shared reality. To develop the completeness of our understanding of this reality it is necessary to combine these representations. The second research contribution reports on a novel methodology for a CIM-based model repository to align PSA representations and provide a knowledge resource for building utility business intelligence of the grid. The third contribution addresses the need for greater integration of information relating to energy storage, an essential aspect of smart energy management. It presents the strategic rationale for integrated energy modeling and a novel extension to the existing CIM standards for modeling grid-scale energy storage. Significantly, this work has already contributed to a larger body of work on modeling Distributed Energy Resources currently under development at the Electric Power Research Institute (EPRI) in the USA.Dr. Martin Bradley on behalf of National Grid Plc. and the Engineering and Physical Sciences Research Council (EPSRC

HOMEBOTS: Intelligent Decentralized Services for Energy Management

The deregulation of the European energy market, combined with emerging advanced capabilities of information technology, provides strategic opportunities for new knowledge-oriented services on the power grid. HOMEBOTS is the namewe have coined for one of these innovative services: decentralized power load management at the customer side, automatically carried out by a `society' of interactive household, industrial and utility equipment. They act as independent intelligent agents that communicate and negotiate in a computational market economy. The knowledge and competence aspects of this application are discussed, using an improved version of task analysis according to the COMMONKADS knowledge methodology. Illustrated by simulation results, we indicate how customer knowledge can be mobilized to achieve joint goals of cost and energy savings. General implications for knowledge creation and its management are discussed

AgentOWL: Semantic Knowledge Model and Agent Architecture

MAS is a powerful paradigm in nowadays distributed systems, however its disadvantage is that it lacks the interconnection with semantic web standards such as OWL. The aim of this article is to present a semantic knowledge model of an agent suitable for discrete environments as well as implementation and a use of such model using the Jena semantic web library and the JADE agent system. The developed library allows interconnection of Agent and Semantic Web technologies can be used in an agent based application where such interconnection is needed. The defined model and methodology show the use of the library in knowledge management applications where the proposed model has been used and evaluated in the scope of the Pellucid and K-Wf Grid IST projects

Barbadian teachers\u27 personal practical knowledge about advocated pedagogic practices used in the education of the under-fives

This study investigated the personal practical knowledge of twenty-one Barbadian teachers in relation to a range of pedagogic practices advocated for use in the education of children under five years of age. The investigation of this knowledge was based on an interpretative perspective. The conceptual underpinning was framed by Personal Construct Psychology (Kelly, 1995), and its methodology, the repertory grid technique. The grid was formulated and used in a sample of schools with under-fives. Findings were clarified, confirmed and elaborated by the use of in-depth interviews conducted with teachers in their classroom settings. The findings revealed that teachers construed pedagogic practices from two perspectives. First, those concerned with the total development. The factors associating the practices and the perspectives were presented under five major themes:- Consideration of the Child; Benefits to the under-fives; Classroom Experiences; Traditional Academic Focus; and Teacher’s versus Child Dominance. Eclectic constructions and uses of teaching practices were clearly evident. Individual choices were varied and at times conflicting; they derived from the teachers’ own construct systems, their anticipation of events in early childhood education, and their technical, cultural and theoretical knowledge. The implications and recommendations made in this study provide the basis for the development of coherent teaching strategies for early childhood education in Barbados

An Adaptive Row Crops Path Generator with Deep Learning Synergy

The autonomous navigation of agricultural field machines strongly depends on the global path generation system. Indeed, a correct and effective path construction heavily influences the overall navigation stack compromising the successfulness of the robot mission. However, the most commonly used search algorithms struggle to adapt to environments where a significant prior knowledge of the domain is not negligible. Despite this crucial factor, path generation for row-based crops has received little attention from the research community so far. The proposed research introduces a novel global path planning system that works in synergy with a deep learning model to provide an accurate and centered path with respect to the rows of the analyzed crop. It guarantees the full coverage of the given occupancy grid with less processing time compared to other available literature solutions. Moreover, the presented methodology can detect an anomaly in the path generation and provide the hypothetical user feedback of the missing full coverage of the given crop. Indeed, especially in a practical application, the correct coverage and centrality of the path are essential for effective autonomous navigation. Experimentation with synthetic and real-world satellite occupancy grid maps clearly show the advantages of the proposed methodology and its intrinsic robustness

A review of the enabling methodologies for knowledge discovery from smart grids data

The large-scale deployment of pervasive sensors and decentralized computing in modern smart grids is expected to exponentially increase the volume of data exchanged by power system applications. In this context, the research for scalable and flexible methodologies aimed at supporting rapid decisions in a data rich, but information limited environment represents a relevant issue to address. To this aim, this paper investigates the role of Knowledge Discovery from massive Datasets in smart grid computing, exploring its various application fields by considering the power system stakeholder available data and knowledge extraction needs. In particular, the aim of this paper is dual. In the first part, the authors summarize the most recent activities developed in this field by the Task Force on “Enabling Paradigms for High-Performance Computing in Wide Area Monitoring Protective and Control Systems” of the IEEE PSOPE Technologies and Innovation Subcommittee. Differently, in the second part, the authors propose the development of a data-driven forecasting methodology, which is modeled by considering the fundamental principles of Knowledge Discovery Process data workflow. Furthermore, the described methodology is applied to solve the load forecasting problem for a complex user case, in order to emphasize the potential role of knowledge discovery in supporting post processing analysis in data-rich environments, as feedback for the improvement of the forecasting performances

A Holistic Approach to Forecasting Wholesale Energy Market Prices

Electricity market price predictions enable energy market participants to shape their consumption or supply while meeting their economic and environmental objectives. By utilizing the basic properties of the supply-demand matching process performed by grid operators, known as Optimal Power Flow (OPF), we develop a methodology to recover energy market's structure and predict the resulting nodal prices by using only publicly available data, specifically grid-wide generation type mix, system load, and historical prices. Our methodology uses the latest advancements in statistical learning to cope with high dimensional and sparse real power grid topologies, as well as scarce, public market data, while exploiting structural characteristics of the underlying OPF mechanism. Rigorous validations using the Southwest Power Pool (SPP) market data reveal a strong correlation between the grid level mix and corresponding market prices, resulting in accurate day-ahead predictions of real time prices. The proposed approach demonstrates remarkable proximity to the state-of-the-art industry benchmark while assuming a fully decentralized, market-participant perspective. Finally, we recognize the limitations of the proposed and other evaluated methodologies in predicting large price spike values.Comment: 14 pages, 14 figures. Accepted for publication in IEEE Transactions on Power System