Registration Deadline Expired!!

The Global BIG DATA Analytics In Power & Utilities Industry Forum 27-28-29 November 2023

The Global BIG DATA Analytics In Power & Utilities Industry Forum is a conference or summit that brings together experts, professionals, and stakeholders in the power and utilities industry to discuss the latest trends, developments, and innovations in big data analytics. The forum typically covers a range of topics, including data management, analytics, visualization, artificial intelligence, and machine learning, as they relate to the power and utilities sector. The objective of the forum is to share insights, best practices, and practical strategies to help organizations in the industry harness the power of big data to optimize their operations, improve efficiency, reduce costs, and enhance customer experience.

09:00 - 09:45

CASE STUDY: WHAT MAKES MAJOR PROJECTS SUCCESSFUL?

Data Science needs : The right mindset The right technology The right methodology The right team

Abed Ajraou
09:45 - 10:30

Cloud Data and Analytics Architecture: Data Everywhere for Everyone

• Cloud platforms provide unique challenges and opportunities to design and architect an optimal Data and Analytics architecture Modernize your analytics and BI capabilities by selecting the products that best meet your needs. How to architect data and analytics stack

Michal Hodinka
10:30 - 11:00

COFFEE BREAK

11:00 - 11:45

Smart Load Management Systems

Smart Load Management System concept. Designs of smart load management systems that can effectively be utilized during emergency energy demand Strategies to efficiently manage energy loads by energy and utility companies in the strategic balancing of energy demand Developing smart load management systems that permit end-to-end network management through advanced control systems Utilising the " Big Data", tools and strategies available through the following sources in drawing the policies Smart Load Management Systems: o Real Time SCADA data o Real Time Quality Management System Data in Smart Load Management Systems o Historical data warehouse systems o CIM data (Common Information Model) o Demand Side Management policies. o Short and long Term Load Forecasts o Artificial intelligence and business Indolence. Impact of intermittence renewable Energy sources on the Load Management System. Energy Market Systems impact on the Smart Load Management Systems. End customer prospective of the load side Management.

Dr Kamal Radi
Brian Magee
11:45 - 12:30

Decreasing lead time for connection upgrades with the help of computer vision @ Alliander

• How AI helps with the planning of your engineers • Image recognition of assets in customers' homes

Malte Lorbach
12:30 - 13:30

LUNCH

13:30 - 14:15

Data opportunities throughout the energy lifecycle

• Generation: AI to improve decision making, production rates and maintenance tasks. • Networks: using Big Data & AI as a core technology for even a smarter grid. • Retail: AI at the core of smart solutions to improve customer experience. • Quantum Technologies: the next big thing

Rodriguez Asensio, Miguel
14:15 - 15:00

Data Journey at Netze BW

• Data governance as a fundamental basis for data management • Explanation of roles like data steward, data officers and tools to measure for instance data quality in our core systems like geoinformation systems, network management systems, SAP PM, SAP IS-U • Setting up and development of a centre of competence in data analytics. What kind of approach, capabilities and IT-platforms are necessary. • Selected Use Cases in the field of a distribution grid operator: e.g. predictive maintenance of gas pipelines and medium voltage grids, optimization of outage locations in medium voltage grids, digital twins of assets. This can be shown in our live systems

Dr. Tobias Krauss
15:00 - 15:30

COFFEE BREAK

15:30 - 16:15

wind farm operations and maintenance (O&M) with digitalization

• Importance of digitalization in the wind sector • Digitalization at ZF Wind Power • Enhancing wind farm operations and maintenance (O&M) with digitalization

Mihail Ivanov
16:15 - 17:00

Why do most of the Data Science projects fail?

• Do you know that more than 85% of Data Science projects fail? • Do you want to avoid becoming part of the statistics? • In this session we would talk about common pitfalls and how to avoid them

Fawad A. Qureshi

09:00 - 09:45

Implementing a corporate Data Science strategy in a integrated Energy Company

• Galp overview • Data Scientist role and the DS Teams organization • The Data Science journey – Ideation and PoCs; Projects; Product • Collaborative & Agile developments • Main challenges & Way-Forward

Frederico Cabral
09:45 - 10:30

Sustainable living through data science

www.amsterdam-energy-forum.com / registration@bigdata-nrg.com Kaustav Basu Lead data Scientist Eneco Sustainable living through data science Eneco is a leading energy utility company based in the Netherlands. - Going beyond being a commodity supplier by offering energy services technology. - Customized energy insight services for over a million customers

Kaustav Basu
11:00 - 11:45

Free Slot

11:45 - 12:30

Big Data in Photovoltaics: from PV plants to self-consumption units

Main differences between the supervision of a PV plant and a DG self-consumption park Data Sources, Models and KPI Fault detection and prediction

12:30 - 13:30

LUNCH

13:30 - 14:15

Assurance of Digital Twins

A digital twin is a virtual representation of a system or asset, that calculates system states and makes system information available, through integrated models and data, with the purpose of providing decision support, over its lifecycle. The Energy industry has used digital twins for a long time, be it under different names, for example grid modelling tools, SCADA systems, and power flow models. Upcoming capabilities related to sensoring, data storage and data analytics (AI/ML) will enable Digital Twins to play an ever increasing role in efficient decision support for saving cost and driving innovation. Examples of key drivers include: Operational efficiency Remote operations Supporting sustainability goals The market for digital twins is likely to grow with a factor of 3 from 2021 to 2026. Digital twins differ in scale and complexity. Different capability levels can be defined for the functional element of a digital twin mapped to the previously mentioned evolution of the functional element. The higher capability, the more value. But as the complexity increases, so does the risk that the digital twin may not deliver what buyers expect, and could leave operators wondering if they can trust information from a twin. DNV recommends that the following four aspects should be considered when assessing trustworthiness of a digital twin: The organizational maturity – an assessment of the organization’s capabilities to transform digitally, including people, tools, technology, processes and competence to develop and maintain qualified digital twins. The quality of the digital twin – assess that the digital twin meets the stated requirements and with the right quality. Risk of use – assess the risk of using digital twins to support decisions. Continuous assurance – ensure and assess that digital twins remain qualified over the lifetime of the asset

14:15 - 15:00

Industry outlook and Latest market intelligence around Digital Transformations

• Where is the investment being focused? • Where are we seeing the challenges? • What are the lessons to be learned, and • What enablers will Enterprises need in terms of strategy, leadership, employee skills and IT infrastructure?

15:00 - 15:30

COFFEE BREA

15:30 - 16:15

Data Governance and Management Journey of E.ON

Introduction About E.ON’s organization and complexity of it Data Governance and Management Journey of the E.ON Group Key Challenges and Lessons Learned

Romina Medici
16:15 - 17:00

The Path towards Enel Platformization

What were the key enablers of Enel's digital strategy The technological drivers paradigms that Enel has adopted to become a Platform company

Matteo Masotti

09:00 - 09:30

Implementing a corporate Data Science strategy in a integrated Energy Company

Towards an energy data market Why is data sharing an important matter in energy systems? What is the value of data and how can it be used as an incentive for data sharing? What are the KPIs and concerns in designing an efficient and secure energy data market? What are the feasible data sharing mechanisms (peer2 peer, central,.) ?

Dr Yashar GhiassiFarrokhfal
09:30 - 10:30

Direct and indirect control of thermal process engineering with a neural network

•Improvement of operations from thermal process engineering with Artificial Intelligence (AI) • Realized optimizations in a pilot project • Practical examples for AI-Prediction and AI-Operator • Data security • Difference between the Uniper-AI-solution and common AI-solutions

Frank Gebhardt
10:30 - 11:00

COFFEE BREAK

11:45 - 12:30

João Fontes Machado
13:30 - 14:15

Theo Borst
14:15 - 15:00

Shubham Rajvanshi

Attendee List

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Chakib Zayoud

  • Date : November 27, 2023 - November 29, 2023
  • Time : 09:00 - 18:00 (Europe/Prague)
  • Reg. Deadline : November 28, 2023 12:00 am