Code for Paper “EdgeHEM: Sparse Federated Reinforcement Learning for Home Energy Management at the Edge”
With the growth of energy demand and the popularization of distributed energy resources, home energy management (HEM) has been emerging as a crucial technology for improving energy efficiency and reducing electricity costs. HEM systems at the edge can provide more efficient and personalized energy services for households through real-time intelligent analysis. This paper proposes EdgeHEM, an edge reinforcement learning framework for HEM that considers the memory constraints of edge devices. Specifically, a dynamic sparse learning strategy with topology evolution is explored to overcome the memory limitations on the network scale. Furthermore, a compressed federated learning approach with gradient approximation is developed to leverage the transitions cached in the memory of multiple edge devices.
The current version supports the following datasets in New York region and tasks are saved in dataset:
- Pecan Street: residential load consumption and PV generation for individual homes
- NYISO: real-time electricity price of the competitve electrc marketplace
- Dark Sky: temperature data derived from various sourcesYou can evaluate this code with Python 3.10+ and PyTorch 1.4.0+.
To use the provided code, please run test.ipynb to obtain the results of the proposed method and benchmark methods.
Note: Please select model hyperparameters to be tested before use.
Enable resource-constrained edge devices to execute effective HEM ！
