Teaching backpropagation

An energy engineer turned data scientist. Currently I am the Director at Data Science Retreat - Europe’s most advanced data science bootcamp.

I like building models to optimize the control of energy systems. I am particularly interested in the space that exists between economic and carbon optimization.

I have been an energy engineer on industrial & district energy projects at ENGIE, as well as a data scientist at Tempus Energy. I am currently rounding out a solid technical base with management experience running Data Science Retreat.

Contact me on LinkedIn or via email.

## Mixed integer linear programming of battery storage and combined heat and power

import energypylinear as epl
model = epl.Battery(power=2, capacity=4, efficiency=1.0)
prices = [10, 50, 10, 50, 10]
info = model.optimize(prices, timestep='30min')


## UK and Australian grid data

The Australian grid is a unique combination of high coal penetrations, quality potential renewable resources (and high penetration in South Australia) and a deregulated, volatile electricity market. It also has good data availability - if you know where to look for it.

## Combined heat and power

I spent four years working as an industrial energy engineer, and worked with a lot of CHP plant. energy-py-linear has a CHP model that can be configured with a number of gas and steam turbines, then optimized as a function of gas and electricity prices.

from energypylinear.chp import Boiler, GasTurbine, SteamTurbine

assets = [
GasTurbine(size=10, name='gt1'),
Boiler(size=100, name='blr1'),
Boiler(size=100, name='blr2', efficiency=0.9),
SteamTurbine(size=6, name='st1')
]

info = optimize(
assets,
gas_price=20,
electricity_price=1000,
site_steam_demand=100,
site_power_demand=100,
)


## Energy

I’m an energy engineer at heart. Some of my most popular work is the Energy Basics series - such as the heat equation and kW versus kWh.

I’ve also written about Average versus Marginal Carbon Emissions, the Four Inconvenient Truths of the Clean Energy Transition and the intersection of energy and machine learning.

## Parallelized Cross Entropy Method

github

CEM on CartPole and Pendulum. Parallelized across processes and through batch.

$python cem.py cartpole --num_process 6 --epochs 8 --batch_size 4096$ python cem.py pendulum --num_process 6 --epochs 15 --batch_size 4096


## Talks

April 3 2017 - Berlin Machine Learning Group - A Glance at Q-Learning - meetup page - youtube

June 21 2017 - Data Science Festival - A Glance at Q-Learning - meetup page - youtube

September 3 2018 - Berlin Machine Learning Group - energy-py - meetup page - slides - GitHub repo