# DER Forecast using Privacy Preserving Federated Learning

arXiv: https://arxiv.org/abs/2107.03248 · HTML: https://mlsanity.com/paper/2107.03248/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 45 references that are in the map.
- First posted on arXiv 7 Jul 2021.

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

- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)

## Built on by (most cited first, 10 of 10)

- [A Secure Federated Learning Framework for Residential Short Term Load Forecasting](https://mlsanity.com/paper/2209.14547/) (2022)
- [Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives](https://mlsanity.com/paper/2304.08602/) (2023)
- [Collaborative Policy Learning for Dynamic Scheduling Tasks in Cloud-Edge-Terminal IoT Networks Using Federated Reinforcement Learning](https://mlsanity.com/paper/2307.00541/) (2023)
- [Secure short-term load forecasting for smart grids with transformer-based federated learning](https://mlsanity.com/paper/2310.17477/) (2023)
- [FedDiSC: A Computation-efficient Federated Learning Framework for Power Systems Disturbance and Cyber Attack Discrimination](https://mlsanity.com/paper/2304.03640/) (2023)
- [FedWOA: A Federated Learning Model that uses the Whale Optimization Algorithm for Renewable Energy Prediction](https://mlsanity.com/paper/2309.10337/) (2023)
- [Edge AI for Internet of Energy: Challenges and Perspectives](https://mlsanity.com/paper/2311.16851/) (2023)
- [A Federated learning model for Electric Energy management using Blockchain Technology](https://mlsanity.com/paper/2307.09080/) (2023)
- [EdgeConvEns: Convolutional Ensemble Learning for Edge Intelligence](https://mlsanity.com/paper/2307.14381/) (2023)
- [Improving accuracy and convergence of federated learning edge computing methods for generalized DER forecasting applications in power grid](https://mlsanity.com/paper/2410.10018/) (2024)

## Abstract (arXiv)

With increasing penetration of Distributed Energy Resources (DERs) in grid edge including renewable generation, flexible loads, and storage, accurate prediction of distributed generation and consumption at the consumer level becomes important. However, DER prediction based on the transmission of customer level data, either repeatedly or in large amounts, is not feasible due to privacy concerns. In this paper, a distributed machine learning approach, Federated Learning, is proposed to carry out DER forecasting using a network of IoT nodes, each of which transmits a model of the consumption and generation patterns without revealing consumer data. We consider a simulation study which includes 1000 DERs, and show that our method leads to an accurate prediction of preserve consumer privacy, while still leading to an accurate forecast. We also evaluate grid-specific performance metrics such as load swings and load curtailment and show that our FL algorithm leads to satisfactory performance. Simulations are also performed on the Pecan street dataset to demonstrate the validity of the proposed approach on real data.