# Privacy-preserving Spatiotemporal Scenario Generation of Renewable Energies: A Federated Deep Generative Learning Approach

arXiv: https://arxiv.org/abs/2107.07738 · HTML: https://mlsanity.com/paper/2107.07738/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 32 references that are in the map.
- First posted on arXiv 16 Jul 2021.

## Builds on (most cited first, 9 of 9)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization](https://mlsanity.com/paper/1606.00709/) (2016)
- [An empirical study on evaluation metrics of generative adversarial networks](https://mlsanity.com/paper/1806.07755/) (2018)
- [Model-Free Renewable Scenario Generation Using Generative Adversarial Networks](https://mlsanity.com/paper/1707.09676/) (2017)
- [On the Effectiveness of Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1712.06391/) (2017)
- [A Distributed Probabilistic Modeling Algorithm for the Aggregated Power Forecast Error of Multiple Newly Built Wind Farms](https://mlsanity.com/paper/1812.07515/) (2018)

## Built on by (most cited first, 22 of 22)

- [Wind Power Forecasting Considering Data Privacy Protection: A Federated Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2211.02674/) (2022)
- [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 Review of Federated Learning in Energy Systems](https://mlsanity.com/paper/2208.10941/) (2022)
- [Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach](https://mlsanity.com/paper/2404.03702/) (2024)
- [Customized Load Profiles Synthesis for Electricity Customers Based on Conditional Diffusion Models](https://mlsanity.com/paper/2304.12076/) (2023)
- [Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives](https://mlsanity.com/paper/2304.08602/) (2023)
- [AIGC for Industrial Time Series: From Deep Generative Models to Large Generative Models](https://mlsanity.com/paper/2407.11480/) (2024)
- [Deep learning based on Transformer architecture for power system short-term voltage stability assessment with class imbalance](https://mlsanity.com/paper/2310.11690/) (2023)
- [Bitcoin Transaction Strategy Construction Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2109.14789/) (2021)
- [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)
- [PMU measurements based short-term voltage stability assessment of power systems via deep transfer learning](https://mlsanity.com/paper/2308.03953/) (2023)
- [A review of federated learning in renewable energy applications: Potential, challenges, and future directions](https://mlsanity.com/paper/2312.11220/) (2023)
- [SolarGAN: Synthetic Annual Solar Irradiance Time Series on Urban Building Facades via Deep Generative Networks](https://mlsanity.com/paper/2206.00747/) (2022)
- [Privacy-Preserving Taxi-Demand Prediction Using Federated Learning](https://mlsanity.com/paper/2305.08107/) (2023)
- [SplitVAEs: Decentralized scenario generation from siloed data for stochastic optimization problems](https://mlsanity.com/paper/2409.12328/) (2024)
- [Harnessing Federated Generative Learning for Green and Sustainable Internet of Things](https://mlsanity.com/paper/2407.05915/) (2024)
- [Privacy-preserving household load forecasting based on non-intrusive load monitoring: A federated deep learning approach](https://mlsanity.com/paper/2206.15192/) (2022)
- [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)
- [Explainable Modeling for Wind Power Forecasting: A Glass-Box Approach with High Accuracy](https://mlsanity.com/paper/2310.18629/) (2023)
- [Wind Power Scenario Generation based on the Generalized Dynamic Factor Model and Generative Adversarial Network](https://mlsanity.com/paper/2508.00692/) (2025)
- [ScenGAN: Attention-Intensive Generative Model for Uncertainty-Aware Renewable Scenario Forecasting](https://mlsanity.com/paper/2509.17119/) (2025)
- [QSTAformer: A Quantum-Enhanced Transformer for Robust Short-Term Voltage Stability Assessment against Adversarial Attacks](https://mlsanity.com/paper/2512.09936/) (2025)

## Abstract (arXiv)

Scenario generation is a fundamental and crucial tool for decision-making in power systems with high-penetration renewables. Based on big historical data, a novel federated deep generative learning framework, called Fed-LSGAN, is proposed by integrating federated learning and least square generative adversarial networks (LSGANs) for renewable scenario generation. Specifically, federated learning learns a shared global model in a central server from renewable sites at network edges, which enables the Fed-LSGAN to generate scenarios in a privacy-preserving manner without sacrificing the generation quality by transferring model parameters, rather than all data. Meanwhile, the LSGANs-based deep generative model generates scenarios that conform to the distribution of historical data through fully capturing the spatial-temporal characteristics of renewable powers, which leverages the least squares loss function to improve the training stability and generation quality. The simulation results demonstrate that the proposal manages to generate high-quality renewable scenarios and outperforms the state-of-the-art centralized methods. Besides, an experiment with different federated learning settings is designed and conducted to verify the robustness of our method.