Pre-conference event: May 13

Digitalization of Energy Markets: Cloud, Robotics and Data Analytics

May 13th 2019: Digitalization of Energy Markets: Cloud, Robotics and Data Analytics

Disruptive forces are challenging the status quo in energy markets across the globe, and we see the industry changing faster when some might have anticipated. Understanding opportunities and strategic risks associated with the rise of new technology and digitalized market structure, is now essential and will prepare you for new things that’ll follow.

In this workshop, we will highlight key tools available for organizations and how they can be effectively applied.  

Led by:

Sid Dash, Research Director, CHARTIS RESEARCH
Jonathan Regenstein, Director of Financial Services, RStudio 
Akshay Singh,
Quantitative Analytics Manager, WGL ENERGY
James Orsulak, Senior Director of Business and Sales, DESCARTES LABS 




The journey to profitable digital trading models: Exploring the data infrastructure and methodology needed to rapidly train, test, and deploy proprietary digital trading models

  • Cloud based data storage and management optimized for trading signal development
  • High performance compute infrastructure to support model development, training and deployment
  • Geospatial and alternative data sources and modalities
  • Developing digital supply and demand factor models 101
  • Identifying a major supply disruption prior to market knowledge of the event
  • Bespoke weather forecasting techniques
  • Forecasting rig completions and production in an individual basin 
  • Forecasting a future charter rate for an ocean vessel class
  • Deep dive: Forecasting gasoline import demand using multimodal data fusion and machine learning

James Orsulak, Senior Director of Business and Sales, DESCARTES LABS 




Machine learning in power trading including application of machine learning and neural networks in load forecasting and scheduling

  • Using fuzzy classifier to manage pipeline optimization and electricity network analysis (allow identification of opportunities in congestion ,capacity trading etc)
  • Using machine learning for renewables systems analysis.
  • Using genetic algorithms   for logistics management in physical energy asset (electrical networks , gas ,coal ) using practical examples to illustrate and illuminate how to build genetic algorithms for the above use cases
  • Managing complex data sets and big data: managing standard energy assets data sets such as networks, contracts , asset price curves , drilling info , logistics and supply chain data  in conjunction with very large scale data
  • Introducing graph , array oriented , gpu accclerated , object ,embedded  and other times of novell databases technologies ( including Hadoop and hpc/Hadoop hybrids ) to address the above big data and complex data problems

Sid Dash, Research Director, CHARTIS RESEARCH




Visualizing energy prices with R: From raw data to an interactive dashboard of modeled  relationships

  • The role and practice of data visualization in energy price research, using tools and packages in the R programming language
  • Starting with raw price data, exploring  model and visualizing it, and finally creating an interactive dashboard to house that work in a compelling format
  • Different parts of the R toolchain and how they fit into the broader data science paradigm
  • Live coding examples

Jonathan Regenstein, Director of Financial Services, RStudio




Using data science and machine learning for energy investments and risks analysis

  • What is the existing approach?
  • What are the pros and cons?
  • How are the outcomes like?
  • Do we back-test? If yes, how does it compare to the inception?
    - What could be wish list items?
    - How do we identify data sources & collect data?
    - What modeling approaches are considered?
    - How could we leverage data science & machine learning (Artificial Intelligence)?
  • Discuss a practical example and future possibilities

Akshay Singh, Manager, Quantitative Analysis, WASHINGTON GAS



Each presenter will summarize key takeaways form their presentations and will open up for the Q&A and final thoughts on the industry’s adoption of digital tools


End of the workshop.