A solar forecasting framework based on federated learning and distributed computing

Haoran Wen, Yang Du*, Eng Gee Lim, Huiqing Wen, Ke Yan, Xingshuo Li, Lin Jiang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


Solar forecasting is a crucial and cost-effective tool for better utilization of solar energy for smart environment design. Artificial intelligence (AI) technologies, such as machine learning (ML) and deep learning (DL), have gained great popularity and been widely applied in solar forecasting in recent years. However, conventional AI-based forecasting methods suffer from high variability and stochasticity of solar irradiation, making unreliable predictions due to heterogeneous solar resources. Moreover, the training process of DL models is less flexible and requires immense data. Even for a well-trained model, it can still yield deteriorated performances on other datasets of varying data distributions. To tackle the deficiencies of AI forecasting models, we present a flexible distributed solar forecasting framework based on a novel spatial and temporal attention-based neural network (STANN) in conjunction with federated learning (FL) technique, considering multi-horizon forecasting scenario from 5–30 min. The STANN model consists of a feature extractor and a forecaster, which can be respectively trained on various local datasets for better localization, and updated to further improve forecasting accuracy through global parameter aggregation under the proposed framework without data gathering. We evaluate effectiveness of the proposed method by conducting extensive experiments on real-world datasets and compare it to other popular forecasting models. The results demonstrate that our approach outperforms the other benchmarks with higher forecasting accuracy for all forecast horizons and better generalization on various datasets, achieving the highest forecast skill of 28.83% at 30 min horizon and an improvement of 11.2% compared with the centralized, localized, and conventional FL training methods.

Original languageEnglish
Article number109556
JournalBuilding and Environment
Publication statusPublished - Nov 2022


  • Deep learning
  • Distributed computing
  • Federated learning
  • Solar forecasting
  • Sustainable environment


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