If you’ve ever wanted to see the impact that machine learning is having in the energy sector, then I recommend watching this seminar released by the National Renewable Energy Laboratory (NREL).
Each talk describes an application of machine learning in the industry at different levels, from the big (weather and climate modelling) through to the small (optimising the aerodynamics of turbine blades).
Some of the topics discussed include:
- How researchers at NREL are using generative adversarial networks (GANs) to assist them with weather and climate modelling
- How you can represent a wind farm as a graph neural network (GNN) with directed edges (this is brilliant!)
- How hard it is to acquire enough data to train models for wind farms (this is why they mention having success with ensemble-based modelling approaches)
- How they’ve been creating simulations to augment their wind farm datasets
- A few key points which I agree with from personal experience
- Features matter more than models – having enough input data, processed in the right way often matters more than the specific machine learning algorithm you’re using
- Training models is expensive and time consuming, but once that stage is done, you can run them cheaply and quickly in production
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Currently fascinated by energy markets and electrical engineering.
In another life I was a beekeeper that did a PhD in computational biology writing image analysis software and using machine learning to quantify honeybee behaviour in the hive.
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