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Conference Programme

Conference Day One | Tuesday 6th June 2017

Registration and refreshments

Opening address from the Chair
Bas Kruimer, Senior Manager, Smart Grid Services - Accenture

Business Case - securing board investment to expand the data analytics function and effectively serve the wider smart utility organisation
  • Establishing the key drivers for board investment in data analytics growth and determining the best way to present this information
  • Identifying the optimal choice of technology platform to serve immediate and long term needs
  • Leveraging standards to ensure the cost-effective integration and interworking of platforms, software, internal and external data
  • Determining the optimal mix of skill-sets required to expand the function and implementing appropriate recruitment and training strategies to support this
  • Identifying and prioritising the use-cases that will enable rapid scale-up whilst securing a healthy return on investment
  • Establishing a data governance framework to support the effective management and utilisation of the large volumes of data generated across the grid
  • Partnering with external parties such as Cloud providers for speed of implementation, cost-efficiency and scalability
Robin Hagemans, Manager Data & Insights - Alliander
Werner Van Westering, Data Scientist - Alliander

Innovative Use-Cases - identifying and prioritising next generation use-cases that will deliver immediate and long term economic value
  • Examining experiences with established use-cases and quantifying the operational and business benefits achieved
  • Identifying new grid related processes that would benefit significantly from the application of data analytics
  • Identifying the technology, software, standardisation, and partnership requirements to fully integrate new use cases
  • Overcoming the challenges associated with ingesting, modelling, and cleansing multiple large data sources to support use-case sophistication and expansion
  • Leveraging next generation technology to marshal data from across the grid including meter, asset, operational, historical and real-time sources
  • Developing a business plan to achieve board approval for new use cases that support grid management and efficiency
Andy Gay, Utilities Segment Lead - GE

Fault Management Use-Case - integrating high volumes of weather data to create advanced data models providing live predications which aid operational decision making
  • Examining the customer service and business drivers that led to the development of an advanced fault management data model
  • Understanding and overcoming the challenges of integrating weather conditions into the data model
  • Evaluating the systems and processes implemented to ease the integration of multiple data sources, ensure speedy analytics, and provide accurate live predictions to operations teams
  • Business implementation and assessing the benefits achieved
David MacLeman, Innovation Strategy Manager - Scottish and Southern Electricity Networks
Adam Brown, CEO - Bellrock Technology

Morning Refreshments and Exhibition

Organisational Structures - establishing a cohesive analytics function that is distributed across multiple departments
  • Examining the pros and cons of centralised vs distributed organisational structures when migrating toward a data centric utility
  • Establishing how a hybrid model can better meet both functional and business needs
  • Optimising the hybrid model to strike the balance between building deep analytics capability at speed whilst maintaining utility business knowledge
  • Determining the infrastructure requirements to effectively support hybrid working
  • Maintaining speed when driving new analytics initiatives through a hybrid organisational model
Raido Rosenberg, Head of Network Systems Development - Elektrilevi OÜ

Analytics Platform - integrating a highly functional, flexible and scalable analytics platform with legacy systems to drive reliable and real-time analytics capability
  • Evaluating the range of platforms on the market and identifying the optimal solution to support smart grid requirements
  • Determining a system architecture that maximises performance whilst minimising complexity
  • Prioritising the functions that will meet immediate use-case requirements, whilst retaining the flexibility and scalability to grow platform capability in line with use-case expansion
  • Effectively supporting rapidly increasing volumes of data generated by IoT
  • Overcoming the challenges of integrating the platform with Cloud services
Harri Hauta-Aho, IT Service Area Manager - Caruna

Open Source Architectures - overcoming the constraints of open source platforms to lay the foundations for state of the art and future-proofed analytics
  • Understanding the principles of open source software and its relevance for the smart utility data analytics environment
  • Examining the risks, challenges and opportunities of utilising open source software for smart grid data analytics
  • Determining the use-cases that are best supported through open source software
  • Mapping the long-term potential of open source software as data analytics requirements evolve
Sander Jansen, Data Architect - Alliander

Lunch and Networking

Cloud Analytics - leveraging Cloud services for instant access to sophisticated and cost-effective analytics capability
  • Evaluating the range of Cloud services on the market tailored to the specific needs of smart utilities and identifying the most functional and cost-effective option for your organisation
  • Establishing the technology challenges associated with the use of Cloud services for smart grid related data analytics and identifying how these are being overcome
  • Working with country specific regulations to ensure data management compliance within the Cloud
  • Managing staff access to Cloud services without compromising customer data privacy and security
  • Establishing the skill set required to effectively manage Cloud services and ensuring this competence is retained internally
Karim Jawad, Tiger Watson IoT Europe, Energy & Utilities - IBM

Data Preparation - developing a robust governance framework for gathering, structuring, storing and utilising multiple data sources from across the smart grid
  • Examining the challenges associated with gathering and storing data from multiple sources across the smart grid and utility organisation
  • Evaluating the governance requirements for both structured and unstructured, internal and external data to ensure consistency of data quality
  • Leveraging standards to ensure ongoing data quality management
  • Dealing with rapidly increasing volumes of data as IoT becomes widely deployed
  • Developing KPIs to ensure the trustworthiness of data from a wide range of sources
  • Supporting the influx of new data from sources such as mobile phones, social media, and other external sources
Theo Borst, Head of Section, Energy - DNV GL

Data Models - identifying the most effective data modelling techniques for the smart grid and fine-tuning algorithms to guarantee results accuracy
  • Comparing tried and tested data models and their suitability for the smart grid environment; relational models, linear programming, generalised models, time series models, statistical models and more
  • Selecting the best technique for a variety of use-cases
  • Overcoming the challenges of working with unstructured and poor quality data
  • Utilising artificial intelligence and machine learning to maximise data analytics efficiency and accuracy
Fiona Fulton, Smart Systems Manager - SP Energy Networks
Alex White, Smart Grid Director, Consulting - CGI

Afternoon Refreshments and Networking

Advanced Algorithms - creating new algorithms that ensure results accuracy for advanced use-cases in the absence of extensive data sets
  • Understanding how gaps in existing algorithms are hindering results accuracy
  • Identifying strategies for fine-tuning existing algorithms to better support results accuracy in the absence of comprehensive high quality data sets
  • Leveraging artificial intelligence to support the ongoing development of the algorithm once it has been applied in practice
Rune van der Meijden, Researcher - Stedin

Data Correlation - effectively combining historical, real-time, external and social media data to maximise analytics accuracy
  • Identifying the optimal mix of data sources to correlate and determining how best to utilise unstructured and complex data sources
  • Developing a model that effectively handles multiple dimensions with ease
  • Overcoming the challenges of integrating social media data into the mix
  • Evaluating the latest tools available for data correlation and identifying the best ways to integrate these with existing infrastructure
  • Leveraging advanced visualisation techniques to bring new intelligence to life and support action immediacy
  • Building up the appropriate analytics skill sets of domain experts and creating a path for effective interworking of data and domain teams
Francisco Melo, Head of Mission Critical Application Development - EDP Distribuição

Roundtable discussions - during this session he audience breaks out into several smaller working groups, each focused on specific themes that arose during the day’s presentations. Each working group will comprise of representatives of the entire smart grid technical community to ensure a well-rounded and holistic discussion. Key issues raised and solutions proposed will be collated for presentation to the wider group at the end of the session.

Networking Reception - time to relax and unwind after an intensive day of presentations and discussion! All participants are invited to join this networking reception where you will have the opportunity to enjoy the company of colleagues from across the European smart grid technical community.

Close of conference day one

Conference Day Two | Wednesday 7th June 2017

Registration and refreshments

Welcome back from the Chair
Thijs van den Broek, Senior Manager, Analytics Advisory - Accenture

Data Visualisation – turning high volumes of complex and varied data into user-friendly visuals that communicate effectively and support rapid decision making and action
  • Establishing the optimal analytics and visualisation methodologies to support grid planning and operation for a single network as well as multiple networks for comparison
  • Achieving speed of visualisation when dealing with unstructured data sets from multiple sources across the grid
  • Optimising visualisation interactivity to ensure data can be fully explored and anomalies identified effectively
  • Ensuring effective interworking of data science and grid engineering colleagues to maximise exploration effectiveness
  • Improving the visualisation tool to ensure its robustness for additional applications and key performance indicators
  • Reviewing the range of visualisation tools and open-source libraries on the market and how they can be adapted for grid analytics
Gordon Jahn, CTO - Open Grid Systems

Real-time Analytics – establishing the platforms and processes to support cost-effective data streaming and real-time analytics in the smart grid
  • Prioritising the key use-cases that will drive investment in real-time analytics
  • Identifying the complete range of internal data that can be leveraged for real-time analytics
  • Evaluating how best to connect with external sources of data and integrate those with your real-time analytics system
  • Identifying the most critical functionalities and determining whether to build or buy the platforms and systems to support real-time analytics
  • Achieving effective integration of the real-time system with other internal systems
  • Building in effective visualisation capability to suit the needs of both data and domain teams
Floran Stuijt, Analytics Lead Architect - Alliander

Asset Management Use-Case – devising an analytics model to support effective asset life-cycle management, grid planning and investment decisions
  • Quantifying the benefits of data analytics for asset investment planning, network design, procurement, installation and commissioning, operations and maintenance, decommissioning and disposal
  • Developing an analytics platform that integrates legacy systems, multiple data flows in high volumes, and structured and unstructured data with ease
  • Correlating multiple data streams including live condition data, historical data, and weather feeds to maximise accuracy
  • Building rules to trigger automatic work order and optimise the maintenance process
Signe Bramming Andersen, Senior Manager, Head of Asset & Energy Management - DONG Energy

Morning Refreshments and Networking

Control Centre Use-Case – developing an analytics model to achieve effective power quality and rapid outage management
  • Quantifying the extent to which data analytics is improving power quality and outage management activities within smart utilities
  • Determining the internal and external data sources required to support rapid analysis, visualisation and decision making
  • Evaluating the potential of new data models and algorithms to maximise results accuracy
  • Overcoming the platform, data integration, and visualisation challenges around real-time analytics for power quality and outage management
  • Upskilling existing staff and introducing new roles into the team to optimise the analytics process
Jesper Vinther Christensen, CEO & Founder -Similix

Technology Innovation Panel – evaluating the latest innovations in data analytics platforms and tools designed specifically for the smart utility environment
During this session, each technology innovator will give a 15-minute presentation on results achieved from the application of their solution in the smart utility environment, as well as their research and development activity to meet future utility needs. The presentations will be followed by 30 minutes of Q&A and panel discussion, whereby you will get the opportunity to quiz the tech experts, understand their innovation plans more fully, and influence the direction of new product development to better meet your data analytics requirements.
Fivos Maniatakos, CEO -sensewaves

Lunch and Networking

Privacy & Security – overcoming the privacy and security vulnerabilities of analytics platforms that are integrated with a range of legacy systems and utilise cloud services
  • Updating on the latest regulation regarding consumer data privacy and understand the implications of this for grid analytics infrastructures and strategies
  • Planning, managing and communicating data policies and rigorous processes for client data protection
  • Implementing measures to combat the security vulnerabilities introduced by system integration
  • Balancing the need for security with the need for ease of data access
Martijn Imrich, Partner,Xomnia

Asset Maintenance Use-Case – establishing an analytics capability to drive condition monitoring and predictive maintenance, improve asset performance and reduce maintenance costs
  • Evaluating the range of data required for effective monitoring and maintenance of critical assets to ensure high levels of power availability and reliability
  • Dealing with data quality challenges such as duplicate data, different time stamps, conflicting information in multiple systems
  • Effectively utilising historic and current data sets in creating rules and predictive models to accurately predict future events
  • Determining the platform requirements to support effective data analysis and decision making for asset maintenance
  • Maintaining results accuracy as data volumes and complexity increase
Elmer Koene, Senior Manager, Digital Asset Management ,Accenture

Afternoon Refreshments and Networking

Operations Use-Case – creating an analytics model to support cost-effective field force management
  • Quantifying the wastage of current field force planning and management and determining the extent to which data analytics would improve this
  • Determining the data types and volumes required for accurate and effective field force planning, prioritisation and scheduling
  • Developing a data model and set of algorithms that accurately optimises field crew management and avoids the need for re-scheduling of planned maintenance activity
  • Evaluating the data analytics infrastructure requirements and identifying how this must evolve to support changing field force management practices
  • Building in the capability to identify safety risks and implement mitigation approaches
Kim Vrancken, Senior Manager, Head of Assets & Grid Architecture - Eandis
Dieter Vonken, Manager, Asset Management Excellence & Data Analytics - Deloitte

Renewables Use-Case – implementing an analytics model to ensure the speed and accuracy of generation forecasting as the ratio of renewable generation increases
  • Introducing advanced power and weather modelling technology on to combine weather predictions and analytics to accurately forecast the availability of wind and solar
  • Effectively utilising temporal and spatial data by merging multiple data sources
  • Overcoming the intermittency issues related to wind and solar generation in maximising forecast accuracy
  • Applying a data model and set of algorithms that support increasing ratios of renewables power generation into the grid as well as enhance grid operations
Matthias Stifter, Scientist, Energy Department - Austrian Institute of Technology

Closing Remarks from the Chair and End of Conference

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