From Vim & QWERTY to Neovim & DVORAK
And never back again.
Recent Posts
Avoiding Simultaneous Import and Export Electricity in Linear Programs
How to avoid simultaneous import and export electricity in linear programs of energy systems.
Measuring Forecast Accuracy with Linear Programming
Using optimization of a battery to measure forecast accuracy.
Mistakes Data Scientists Make
Badges of honour for the accomplished data scientist.
Daniel C. Dennett’s Four Competences
A useful idea to understand computational control algorithms.
Space Between Money and the Planet
The opportunity cost for using batteries to reduce carbon emissions.
Introducing energy-py-linear
A Python library for optimizing energy systems using mixed integer linear programming.
A Guide to Deep Learning Layers
Explaning the fully connected, convolution, the LSTM and attention deep learning layer architectures.
A Hackers Guide to AEMO & NEM Data
A simple guide to data provided by AEMO for the Australia’s National Electricity Market (NEM).
Typical Year Forecasting of Electricity Prices
Improve your energy project modelling with this simple & flexible forecasting technique.
Jevon’s Paradox
Energy efficiency is not so simple.
Thermodynamics for Energy Engineers
The two laws at the foundation of energy engineering.
Average vs. Marginal Carbon Intensities in Electricity Grids
Many energy professionals are (still!) getting this wrong.
Elexon API UK Electricity Grid Data using Python
Downloading, cleaning & joining UK electricity grid data with pandas, requests and pydantic.
AI, Machine Learning and Deep Learning
What’s the difference between artificial intelligence, machine learning and deep learning?
Warming up with Pipeline Data Engineering Academy
My experience being a part of the Summer Camp - Pipeline’s week long data engineering adventure.
World Models (the long version)
Ha & Schmidhuber’s World Models reimplemented in Tensorflow 2.0.
Setting and resetting LSTM hidden states in Tensorflow 2
Getting control using a stateful and stateless LSTM.
Machine Learning in Energy
A guide for the energy professional.
Cross Entropy Method
A parallelized Python implementation.
Steal This Decarbonization Plan!
Hal Harvey shows the way.
Solving Open AI gym Cartpole using DDQN
Finally - stable learning.
A simple Python workflow for time series simulations
Using the defaultdict to simulate temporal problems.
We won’t solve climate change - and it’s ok
Our evolution shaped psychology is a big reason why.
A Pythonic blockchain
Making use of the Python standard library.
All hail Open AI Five
Dota fans are making the same mistake Go fans did in underestimating Open AI.
Next steps in the clean energy transition
What we need to do next.
DDQN hyperparameter tuning using Open AI gym Cartpole
Tuning hyperparameters of the new energy_py DDQN reinforcement learning agent.
DQN debugging using Open AI gym Cartpole
Debugging the new energy_py DQN reinforcement learning agent.
Getting to Zero: Pathways to Zero Carbon Electricity Systems - Jesse Jenkins
How do we get to a zero carbon world?
The Four Inconvenient Truths of the Clean Energy Transition
All is not well with the clean energy transition.
The State of Energy Storage in America
Whats going on with energy storage?
Demateralization and the clean energy transition
Not every technology trend applies to energy.
Inertia in Electricity Systems
Inertia is a common criticism of the energy transition - is it valid?
11 tips from 11 months of learning Python
I’ve been learning Python for around 11 months - it’s been a wonderful journey!
Getting Wind and Sun onto the Grid
This post covers three interesting insights from the 2017 IEA report Getting Wind and Sun onto the Grid.
CHP Cheat Sheet - Gas Engines & Gas Turbines
Technical modeling of combined heat & power (CHP) plants was my first area of professional specalization.
Blockchain in Energy
An introduction to blockchain the technology.
Elon Musk on autonomous cars
Musk thinks autonomous vehicles will be cheaper and driven for longer.
Machine Learning in Energy - Part Two
Applications of machine learning in energy - forecasting, disaggregation and reinforcement learning.
CAISO Stage 1 Grid Emergency
On May 3rd 2017 the California grid experienced its first Stage 1 grid emergency in nearly a decade.
Machine Learning in Energy - Part One
What is machine learning anyway?
Oil Reserves Growth
The more oil we burn, the more oil reserves we have.
The Complexity of a Zero Carbon Grid
Jesse Jenkin’s insights on decarbonizing electricity systems.
energy-py - reinforcement learning for energy systems
An introduction to the open source energy focused reinforcement learning library energy-py.
Capacity Factor
It’s not how much capacity (MW) you have - it’s how you use it (MWh).
How to save the energy system - André Bardow
Peak oil won’t save the planet, wind and solar lack an inherent economy of scale and German capacity factors.
2017 BP Energy Outlook
BP’s view on the next 40 years.
Average versus Marginal Carbon Emissions
Many professionals are getting this wrong.
A Simple Look at the Efficiency Penalty Cost of Renewables
Can a fossil fuel efficiency penalty offset the benefits of renewables?