Thursday, September 14, 2017

Who's Who in Energy Forecasting: Geert Scholma

I got to know Geert Scholma from NPower Forecasting Challenge 2015, where he outperformed my BigDEAL students on the leaderboard. Since then, he has been topping the NPower leaderboard every time. Recently, as a winner of the qualifying match of GEFCom2017, he presented his methodology at ISEA2017.

Geert lives in Rotterdam, The Netherlands. He has a strong focus on data science and the energy transition, with a masters degree in physics and 5 years experience as an Energy Forecaster for Energy Retail Company and E.On spin-off Uniper Benelux.

Since 2015 he has participated in several online energy forecasting competitions, with the following track record:

What brought you to the energy forecasting profession?

Since an early stage of my physics education at University I have been inspired by developments in the Energy Transition and have directed my career path towards it. This began with research and internships in the field of solar electricity production and energy service companies. My first job was at a consultancy firm, where we managed energy labels and energy policy for social housing firms. I then decided to look for a position at a large energy company in The Netherlands, but it was a coincidence that I ended up as an energy forecaster. I had never heard of the term before, but the field has proven me to be very interesting.

What do you do at your current job? And what's fun about it?

5 years ago I started my job as an energy forecaster for Uniper Benelux. My main focus has been the development of new day-ahead forecasting models for all our customers. As our portfolio consists of electricity, gas and district heating for small, medium and large clients, this is quite a diverse challenge. The main task of our team is to manage the balance responsibility and minimize our clients' imbalance volumes and costs. Besides having to forecast consumption and production volumes, this also means taking into account the effects of hierarchy / portfolio and pricing the profiles of potential new clients. The fun part for me is squeezing the most information out of these big data. And I guess in general, working with numbers just makes me a happy person :)

What was your first (forecasting or data mining) competition about? And how did you do?

My first competition was the first UK Npower competition. Data were a single aggregated daily electricity consumption time series and thus relatively easy to manage as I was used to work with multiple time series with an hourly resolution. I won the competition. As forecasting much more than 1 day into the future was new to me I learned to not extrapolate time trends too enthusiastically into the future. All competitions I have participated so far have always taught me similar lessons that I wouldn't have learned as fast within my daily job.

Can you share with us the most exciting competition you've participated?

The most exciting competition so far was the recent RTE Power Consumption Forecast Challenge in 2017. The task was to forecast the day-ahead 15 minute electricity consumption for all 12 French Regions. The aspect that it made it more interesting than the other competitions was the fact that the data was real and the solution applicable. Also the competition much tougher. The event was concluded with a seminar in Paris where I learned that almost all of my competitors used machine learning, where my solution was mainly based on a single linear regression model.

Is there a key initiative or exciting project you are working on these days?

I am working on an update for the second part of the French RTE Competition this winter. I am focusing on an update of my base model, but also machine learning and ensemble forecasting. I am curious how the battle between simple linear regression and complicated black box machine learning methods will end next time when I include some new variables I already have in mind. Together with someone from IBM we are also working on a new approach to (energy) forecasting benchmarks, but this will still take some more time to become concrete.

What's your forecast for the next 10 years of energy forecasting field?

I expect real-time pricing and demand-side management to become a significant new factor in energy forecasting. One of the current challenges is often still to predict a yearly growing volume of "behind the meter", renewable energy (mostly solar) production. As renewable production will become more and more difficult to manage, market prices for more clients will become flexible and more client groups will be encouraged to either store their own production or shift their demand towards off-peak time hours. I expect this to open a complete new and very interesting chapter in energy forecasting.

How do you spend your free time?

I am a real outdoor sportsman and enjoy cycling and tennis. My partner is from Italy and we often visit her family in Puglia where we enjoy the food, family and beautiful coast and countryside.

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