Get a firsthand look at how we built a lightweight feature store to accelerate electricity grid forecasting. We’ll cover our decision process, design choices, and implementation using Polars and Google Cloud Storage. Expect lessons learned, real-world bumps, and a clear view of the costs, trade-offs and benefits of our solution.
In this talk, we’ll share how we built a lightweight, production-ready feature store to support electricity grid forecasting. You’ll hear a firsthand account of our journey—from identifying the need to accelerating model prototyping through feature standardization and flexibility.
We’ll start with a high-level overview of our decision-making process: why we chose to build rather than buy, and the trade-offs we considered. Then, we’ll dive into the architecture of our custom feature store, detailing how we leveraged Polars for fast processing and Google Cloud Storage as a scalable backend.
Expect an honest look at the challenges we faced, the benefits we gained, and the costs we encountered along the way. Whether you’re considering building your own feature store or just curious about scaling ML for time series problems, this session will offer practical insights and real-world lessons.
Robin Troesch
Data Engineer trying to reduce the impact of computing on the climate and helping the energy transition.
Working at Electricity Maps in Copenhagen (DK) since 2022 first in the data platform team responsible for acquiring grid data.
Joined the grid forecast team in 2023.
Outline
Reflections
Citation
@online{bochman2025,
author = {Bochman, Oren},
title = {Building a {Lightweight} {Feature} {Store} for {Electricity}
{Grid} {Forecasts} with {Polars}},
date = {2025-12-12},
url = {https://orenbochman.github.io/posts/2025/2025-12-11-pydata-lightweight-feat-store/},
langid = {en}
}