Improve your energy project modelling with this simple & flexible forecasting technique.
Energy efficiency is not so simple.
These two laws are the foundation of energy engineering.
Many energy professionals are (still!) getting this wrong.
An introduction to how the UK recovers electricity grid balancing costs.
Downloading, cleaning & joining UK electricity grid data with pandas, requests and pydantic.
Is there an opportunity cost for using batteries to save carbon?
What’s the difference between artificial intelligence, machine learning and deep learning?
Fully connected, convolution, the LSTM and attention deep learning layer architectures explained.
My experience being a part of the Summer Camp - Pipeline’s week long data engineering adventure.
A useful idea to understand computational control algorithms.
Ha & Schmidhuber’s World Models reimplemented in Tensorflow 2.0.
Badges of honour for the accomplished data scientist.
Getting control using a stateful and stateless LSTM.
A guide for the energy professional.
A parallelized Python implementation.
Hal Harvey shows the way.
Using energy-py-linear to measure the economic value of using a forecast.
A library for optimizing energy systems using mixed integer linear programming.
Finally - stable learning.
Using the defaultdict to simulate temporal problems.
Our evolution shaped psychology is a big reason why.
Making use of the Python standard library.
A simple guide to data available for the NEM - an Australian electricity grid & market.
Dota fans are making the same mistake Go fans did in underestimating Open AI.
What we need to do next.
Tuning hyperparameters of the new energy_py DDQN reinforcement learning agent.
Debugging the new energy_py DQN reinforcement learning agent.
How do we get to a zero carbon world?
All is not well with the clean energy transition.
Whats going on with energy storage?
Not every technology trend applies to energy.
Inertia is a common criticism of the energy transition - is it valid?
I’ve been learning Python for around 11 months - it’s been a wonderful journey!
This post covers three interesting insights from the 2017 IEA report Getting Wind and Sun onto the Grid.
Technical modeling of combined heat & power (CHP) plants was my first area of professional specalization.
An introduction to blockchain the technology.
Musk thinks autonomous vehicles will be cheaper and driven for longer.
Applications of machine learning in energy - forecasting, disaggregation and reinforcement learning.
On May 3rd 2017 the California grid experienced its first Stage 1 grid emergency in nearly a decade.
What is machine learning anyway?
The more oil we burn, the more oil reserves we have.
Jesse Jenkin’s insights on decarbonizing electricity systems.
An introduction to the open source energy focused reinforcement learning library energy-py.
It’s not how much capacity (MW) you have - it’s how you use it (MWh).
Peak oil won’t save the planet, wind and solar lack an inherent economy of scale and German capacity factors.
BP’s view on the next 40 years.
Many professionals are getting this wrong.
Can a fossil fuel efficiency penalty offset the benefits of renewables?
How clean are wind and solar really?