# Wind Power Forecasting Considering Data Privacy Protection: A Federated Deep Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/2211.02674 · HTML: https://mlsanity.com/paper/2211.02674/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 39 references that are in the map.
- First posted on arXiv 2 Nov 2022.

## Builds on (most cited first, 15 of 15)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [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)
- [Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach](https://mlsanity.com/paper/2003.08725/) (2020)
- [Detection of False Data Injection Attacks in Smart Grid: A Secure Federated Deep Learning Approach](https://mlsanity.com/paper/2209.00778/) (2022)
- [Privacy-preserving Spatiotemporal Scenario Generation of Renewable Energies: A Federated Deep Generative Learning Approach](https://mlsanity.com/paper/2107.07738/) (2021)
- [Optimal Scheduling of Isolated Microgrids Using Automated Reinforcement Learning-based Multi-period Forecasting](https://mlsanity.com/paper/2108.06764/) (2021)
- [A Deep-Learning Intelligent System Incorporating Data Augmentation for Short-Term Voltage Stability Assessment of Power Systems](https://mlsanity.com/paper/2112.03265/) (2021)
- [A Two-Stage Approach for Combined Heat and Power Economic Emission Dispatch: Combining Multi-Objective Optimization with Integrated Decision Making](https://mlsanity.com/paper/1808.05704/) (2018)
- [Optimal Scheduling of Integrated Demand Response-Enabled Community Integrated Energy Systems in Uncertain Environments](https://mlsanity.com/paper/2108.08055/) (2021)
- [Hierarchical Stochastic Scheduling of Multi-Community Integrated Energy Systems in Uncertain Environments via Stackelberg Game](https://mlsanity.com/paper/2112.07103/) (2021)
- [Joint Planning of Distributed Generations and Energy Storage in Active Distribution Networks: A Bi-Level Programming Approach](https://mlsanity.com/paper/2201.05932/) (2022)
- [Optimal dispatch of low-carbon integrated energy system considering nuclear heating and carbon trading](https://mlsanity.com/paper/2209.12025/) (2022)
- [Optimal scheduling of isolated microgrid with an electric vehicle battery swapping station in multi-stakeholder scenarios: a bi-level programming approach via real-time pricing](https://mlsanity.com/paper/1809.10206/) (2018)
- [A Critical Overview of Privacy-Preserving Approaches for Collaborative Forecasting](https://mlsanity.com/paper/2004.09612/) (2020)

## Built on by (most cited first, 12 of 12)

- [LTPNet Integration of Deep Learning and Environmental Decision Support Systems for Renewable Energy Demand Forecasting](https://mlsanity.com/paper/2410.15286/) (2024)
- [Optimal scheduling of island integrated energy systems considering multi-uncertainties and hydrothermal simultaneous transmission: A deep reinforcement learning approach](https://mlsanity.com/paper/2212.13472/) (2022)
- [A review of federated learning in renewable energy applications: Potential, challenges, and future directions](https://mlsanity.com/paper/2312.11220/) (2023)
- [Wind turbine condition monitoring based on intra- and inter-farm federated learning](https://mlsanity.com/paper/2409.03672/) (2024)
- [Multi-Microgrid Collaborative Optimization Scheduling Using an Improved Multi-Agent Soft Actor-Critic Algorithm](https://mlsanity.com/paper/2304.01223/) (2023)
- [SplitVAEs: Decentralized scenario generation from siloed data for stochastic optimization problems](https://mlsanity.com/paper/2409.12328/) (2024)
- [Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning](https://mlsanity.com/paper/2507.22565/) (2025)
- [ZTFed-MAS2S: A Zero-Trust Federated Learning Framework with Verifiable Privacy and Trust-Aware Aggregation for Wind Power Data Imputation](https://mlsanity.com/paper/2508.18318/) (2025)
- [Predicting Short Term Energy Demand in Smart Grid: A Deep Learning Approach for Integrating Renewable Energy Sources in Line with SDGs 7, 9, and 13](https://mlsanity.com/paper/2304.03997/) (2023)
- [The State of the Art in transformer fault diagnosis with artificial intelligence and Dissolved Gas Analysis: A Review of the Literature](https://mlsanity.com/paper/2304.11880/) (2023)
- [Resilient Control of Networked Microgrids using Vertical Federated Reinforcement Learning: Designs and Real-Time Test-Bed Validations](https://mlsanity.com/paper/2311.12264/) (2023)
- [A Behaviour-Aware Federated Forecasting Framework for Distributed Stand-Alone Wind Turbines](https://mlsanity.com/paper/2603.05263/) (2026)

## Abstract (arXiv)

In a modern power system with an increasing proportion of renewable energy, wind power prediction is crucial to the arrangement of power grid dispatching plans due to the volatility of wind power. However, traditional centralized forecasting methods raise concerns regarding data privacy-preserving and data islands problem. To handle the data privacy and openness, we propose a forecasting scheme that combines federated learning and deep reinforcement learning (DRL) for ultra-short-term wind power forecasting, called federated deep reinforcement learning (FedDRL). Firstly, this paper uses the deep deterministic policy gradient (DDPG) algorithm as the basic forecasting model to improve prediction accuracy. Secondly, we integrate the DDPG forecasting model into the framework of federated learning. The designed FedDRL can obtain an accurate prediction model in a decentralized way by sharing model parameters instead of sharing private data which can avoid sensitive privacy issues. The simulation results show that the proposed FedDRL outperforms the traditional prediction methods in terms of forecasting accuracy. More importantly, while ensuring the forecasting performance, FedDRL can effectively protect the data privacy and relieve the communication pressure compared with the traditional centralized forecasting method. In addition, a simulation with different federated learning parameters is conducted to confirm the robustness of the proposed scheme.