Back to Search
Start Over
Electrical Load Forecasting Using Edge Computing and Federated Learning
- Source :
- ICC
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- In the smart grid, huge amounts of consumption data are used to train deep learning models for applications such as load monitoring and demand response. However, these applications raise concerns regarding security and have high accuracy requirements. In one hand, the data used is privacy-sensitive. For instance, the fine-grained data collected by a smart meter at a consumer's home may reveal information on the appliances and thus the consumer's behaviour at home. On the other hand, the deep learning models require big data volumes with enough variety and to be trained adequately. In this paper, we evaluate the use of Edge computing and federated learning, a decentralized machine learning scheme that allows to increase the volume and diversity of data used to train the deep learning models without compromising privacy. This paper reports, to the best of our knowledge, the first use of federated learning for household load forecasting and achieves promising results. The simulations were done using Tensorflow Federated on the data from 200 houses from Texas, USA.<br />ICC 2020-2020 IEEE International Conference on Communications (ICC)
- Subjects :
- FOS: Computer and information sciences
Scheme (programming language)
Database
Electrical load
business.industry
Computer science
Smart meter
Deep learning
05 social sciences
Big data
050801 communication & media studies
computer.software_genre
Computational Engineering, Finance, and Science (cs.CE)
Demand response
0508 media and communications
Smart grid
0502 economics and business
050211 marketing
Artificial intelligence
Computer Science - Computational Engineering, Finance, and Science
business
computer
Edge computing
computer.programming_language
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- ICC 2020 - 2020 IEEE International Conference on Communications (ICC)
- Accession number :
- edsair.doi.dedup.....8f3a724a9f89e0a458c9cbcd4329dc61