# A Beginner's Guide to Power and Energy Measurement and Estimation for Computing and Machine Learning

arXiv: https://arxiv.org/abs/2412.17830 · HTML: https://mlsanity.com/paper/2412.17830/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2024.

## Built on by (most cited first, 3 of 3)

- [Attention to Detail: Evaluating Energy, Performance, and Accuracy Trade-offs Across vLLM Configurations](https://mlsanity.com/paper/2607.09172/) (2026)
- [BIPPO: Budget-Aware Independent PPO for Energy-Efficient Federated Learning Services](https://mlsanity.com/paper/2511.08142/) (2025)
- [The Battery Price of edge AI: A study of the Environmental Impact of LLM Inference on Mobile Devices](https://mlsanity.com/paper/2609.11940/) (2026)

## Abstract (arXiv)

Concerns about the environmental footprint of machine learning are increasing. While studies of energy use and emissions of ML models are a growing subfield, most ML researchers and developers still do not incorporate energy measurement as part of their work practices. While measuring energy is a crucial step towards reducing carbon footprint, it is also not straightforward. This paper introduces the main considerations necessary for making sound use of energy measurement tools and interpreting energy estimates, including the use of at-the-wall versus on-device measurements, sampling strategies and best practices, common sources of error, and proxy measures. It also contains practical tips and real-world scenarios that illustrate how these considerations come into play. It concludes with a call to action for improving the state of the art of measurement methods and standards for facilitating robust comparisons between diverse hardware and software environments.