Fuzzy Clustered Federated Learning Algorithm for Solar Power Generation Forecasting

Eungeun Yoo, Haneul Ko, Sangheon Pack

Research output: Contribution to journalArticlepeer-review

Abstract

Federated learning (FL) is a promising technique to construct a solar power generation forecasting model based on data collected from local generators. However, a set of local generators (i.e., cluster) for FL should be carefully defined to construct a high-accuracy forecasting model. Herein, we propose a fuzzy clustered FL algorithm (FCFLA) where each local generator can be included in more than one cluster. In FCFLA, a local generator has its own membership degree representing its sense of belonging to a specific cluster. Based on this membership degree, FCFLA can generate the high-accuracy forecasting model by catching different characteristics of the data of local generators while addressing the training data shortage problem. Evaluation results demonstrate that FCFLA has the fastest convergence time in achieving the desired accuracy.

Original languageEnglish
JournalIEEE Transactions on Emerging Topics in Computing
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • Clustering algorithms
  • Data models
  • Federated learning
  • Forecasting
  • Fuzzy logic
  • Generators
  • Predictive models
  • Servers
  • clustering
  • energy
  • generation forecasting
  • solar

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications

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