# Energy Prediction using Federated Learning

arXiv: https://arxiv.org/abs/2301.09165 · HTML: https://mlsanity.com/paper/2301.09165/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 3 references that are in the map.
- First posted on arXiv 22 Jan 2023.

## Builds on (most cited first, 3 of 3)

- [Energy Demand Prediction with Federated Learning for Electric Vehicle Networks](https://mlsanity.com/paper/1909.00907/) (2019)
- [Electrical Load Forecasting Using Edge Computing and Federated Learning](https://mlsanity.com/paper/2201.11248/) (2022)
- [Federated Learning based Energy Demand Prediction with Clustered Aggregation](https://mlsanity.com/paper/2210.15850/) (2022)

## Built on by (most cited first, 2 of 2)

- [Evaluating Multi-Global Server Architecture for Federated Learning](https://mlsanity.com/paper/2311.15382/) (2023)
- [FedCCL: Federated Clustered Continual Learning Framework for Privacy-focused Energy Forecasting](https://mlsanity.com/paper/2504.20282/) (2025)

## Abstract (arXiv)

In this work, we demonstrate the viability of using federated learning to successfully predict energy consumption as well as solar production for all households within a certain network using low-power and low-space consuming embedded devices. We also demonstrate our prediction performance improving over time without the need for sharing private consumer energy data. We simulate a system with four nodes using data for one year to show this.