Predicting Optimal Temperature in The Transmission System

Explore a conference talk on predicting optimal temperature in transmission systems for district heating. Dive into the collaboration between Centralkommunernes Transmissionsselskab (CTR) and neurospace to leverage existing data and Machine Learning for optimizing supply temperature. Learn about the challenges in district heating, the importance of working with domain experts, and the development of two ML models: one for predicting optimal temperature and another for estimating network congestion. Gain insights into the user story, the significance of this approach, and the key takeaways from implementing machine learning in the district heating sector. Discover how this innovative solution aims to provide cheaper and greener district heating while maintaining service obligations.

Intro
User story
Why?
What is "district heating"?
The challenge with district heating
The challenge with machine learning
ML model 1: Predicting optimal supply temperature
ML model 2: Predicting hydraulic limitations
Key takeaways
Outro