Predicting Optimal Temperature in The Transmission System (CTR)
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> Centralkommunernes Transmissionsselskab (CTR) has in collaboration with Neurospace explored whether already existing data and Machine Learning can predict the optimal supply temperature, without risking universal service obligations, while optimizing the supply temperature to provide cheaper and greener district heating. Two Machine Learning models are created; one to predict the optimal temperature, and another to estimate whether the predicted supply temperature causes network congestion. During this presentation, we will discuss the challenge at hand, the significance of working in closely collaboration with domain experts, and the proposed solution.
Transcript
Centralkommunernes Transmissionsselskab (CTR) has in collaboration with Neurospace explored whether already existing data and Machine Learning can predict the optimal supply temperature, without risking universal service obligations, while optimizing the supply temperature to provide cheaper and greener district heating. Two Machine Learning models are created; one to predict the optimal temperature, and another to estimate whether the predicted supply temperature causes network congestion.
During this presentation, we will discuss the challenge at hand, the significance of working in closely collaboration with domain experts, and the proposed solution.
About the speakers
Co-Founder and Machine Learning Engineer at neurospace