We will describe the knowledge we gained in one of our latest projects related to predicting electricity demand on a specific market. We will discuss the challenges we faced, the lessons we learned and rough overview of the implemented solution. The talk will cover the perspectives of both data scientists and data engineers.

1. ARTIFICIAL INTELLIGENCE
THE ENERGY OF THE FUTURE
Jasmina Ovčak, PhD
Senior Data Scientist
Andrés Latas Martínez
Data Engineer
Comtrade Digital Services
Ljubljana, Slovenia

3. Electr icity’s Jour ney
ConsumptionProduction

4. Where is t he Challenge?
Power(MW)
Time
Peak load
Base load

5. Stem offers energy storage as a service to reduce
electricity bills for their customers.
Hand in Hand with AI i n the Ener gy Sector

6. The Solution
Data
Engineering
Data
Science
Develop a predictive approach based on
machine learning
Acquire the data
Integrate the model into client’s
environment

7. Data
Algorithm
The model needs to:
• GAIN EXPERIENCE
through data and interactions, and
• KNOW HOW TO “THINK” AND MAKE DECISIONS
which is defined by an algorithm.

8. Data
Prepared data for the algorithm
Understand
the data
and domain
Process the
data
Identify
data
sources

9. Pr ediction Algor ithm
Demand
prediction
Daily peaks
prediction
Annual peaks
prediction

10. Per iodically Gener ated Pr edictions
FuturePresent
Peak?
demand

11. Two services
Peak prediction
Market demand data
Data
Data
source
Canonical
form
Storage API
Electricity demand data
Historical
Actual

12. 012
SERVICESOURCE STORAGE
API
Electr icity Data Ser vice

13. Peak Pr ediction Ser vice
Data sources
Peak
prediction
Service
Storage
Predictions

14. Conclusion
• “With Comtrade Digital Services and its
innovative and resourceful AI team, we
have a long term collaboration partner who
can support our continued growth in the
field of predictive AI”
Larsh Johnson – CTO at
“Most innovative solution in the energy sector
2019”

15. Copyright © 2019 Comtrade. All rights reserved.
The content of this presentation is copyright protected.
Any reproduction, distribution, or modification is not allowed.
The information, solutions, and opinions contained in this presentation are of informative nature only and are
not intended to be a comprehensive study, nor should they be relied on or treated as a means to provide a
complete solution or advice, since we may not be aware of all specific circumstances of the case. We try to
provide quality information, but we make no claims, promises, or guaranties about the accuracy, completeness,
or adequacy of the information contained herein.