Richard Findlay

Energy & AI

Technologies, Markets and Policy

01

BL0G P0STS

OCT 10, 2022 Deep Reinforcement Learning for Grid Scale Lithium-ion Battery Optimisation Using model-free DRL to optimise wholesale energy arbitrage for grid-scale batteries
JUN 15, 2022 Probabilistic Forecasting of Renewable Generation & Wholesale Prices with Quantile-Regression Sequence-to-sequence networks with spatial-temporal attention
APR 30, 2022 Reinforcement Learning for Offshore Wind Farm Optimisation Applying Q-learning to static and quasi-dynamic wind farm environments to optimise wake steering
02

0THER PR0JECTS

OHMERA.AI ohmera New age energy market intelligence
DONDER.CO.UK donder A hill-walking journal for logging ascents of UK and Irish peaks
OFFICETEA.CO officetea Anonymous stories from inside toxic workplaces
03

AB0UT ME

I'm a data scientist with a strong foundation in renewable energy development, energy storage modelling, and data-driven innovation across the energy sector. Since 2017, I've worked on projects spanning wind farm development, hydro optimisation, and the application of AI and machine learning to accelerate decision-making in clean energy infrastructure.

My current focus lies in the convergence of advanced analytics and energy markets, particularly the optimisation of battery energy storage systems and the role of data science in enhancing short-term trading strategies.

Richard Findlay
FULL PROFILE →
04

READING LIST

Inside the Black Box: A Simple Guide to Quantitative and High-Frequency Trading Quantitative Energy Finance: Modeling, Pricing, and Hedging in Energy and Commodity Markets Python for Algorithmic Trading Cookbook Energy Trading and Risk Management: A Practical Approach to Hedging, Trading and Portfolio Diversification Quantitative Trading: How to Build Your Own Algorithmic Trading Business Energy Trading & Investing: Trading, Risk Management, and Structuring Deals in the Energy Markets Data Science for Wind Energy Advances in Financial Machine Learning Machine Learning Design Patterns Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow Practical MLOps: Operationalizing Machine Learning Models PyTorch Pocket Reference: Building and Deploying Deep Learning Models Effective Python: 90 Specific Ways to Write Better Python Mastering PyTorch: Build Powerful Deep Learning Architectures Using Advanced PyTorch Features The Hundred-Page Machine Learning Book Deep Reinforcement Learning Hands-On Machine Learning with PyTorch and Scikit-Learn Deep Learning (Adaptive Computation and Machine Learning)
FULL LIST →