An interpretable horizontal federated deep learning approach to improve short-term solar irradiance forecasting

• Published in: Journal of cleaner production 2024-01, Vol.436, p.140585, Article 140585
• Main Authors: Xiao, Zenan, Gao, Bixuan, Huang, Xiaoqiao, Chen, Zaiqing, Li, Chengli, Tai, Yonghang
• Format: Article
• Language: English
• Subjects: Data privacy and protection; Deep learning; Horizontal federated framework; Interpretable federated learning; Solar irradiance forecasting
• ISSN: 0959-6526; 1879-1786
• DOI: 10.1016/j.jclepro.2024.140585

Solar irradiance forecasting is critical in the planning and operation of solar power plants for production scheduling, energy trading, and maintenance planning. Accurate solar irradiance forecasting is crucial for efficient and stable power systems. Various deep learning (DL) methods have been researched in solar irradiance forecasting because of their powerful capabilities of nonlinear mapping. However, traditional DL models face challenges with small sample sizes, irradiance fluctuations, and data privacy concerns. On this foundation, it is also critical to ensure the interpretability of artificial intelligence (AI) models, as interpretable and trustworthy predictive models can help operators make effective decisions. Focusing on the above-mentioned issues, a privacy-preserving, interpretable, and DL-based model is proposed to improve the forecast accuracy of solar irradiance while ensuring the data available but not visible to each participant and prediction comprehensibility. This model is constructed by horizontal federated framework (HFF), self-attention (SA) mechanism, convolutional neural network (CNN), and long short-term memory (LSTM) network, named HFF-based SA-CNN-LSTM. A series of comprehensive experiments spanning various climate regions are designed to assess model performance under different federated conditions. The experiments show that this method significantly improves forecasting accuracy, especially in scenarios with incomplete data and highly fluctuating irradiance when federated with other datasets, and the model exhibits interpretability, adjusting attention for precise predictions at different time points. Moreover, the effects of different federated scales on the predictive performance of the target model are fully discussed for future practical applications. These findings hold significant promise for enhancing the accuracy of irradiance forecasting, contributing to energy efficiency, grid stability, and data privacy. [Display omitted] •A horizontal federated framework is proposed for improving irradiance forecasting.•A deep learning (DL) method is proposed based on horizontal federated framework.•The effective of horizontal federated is validated by several experiments.•The interpretability is used in DL methods and the results are analyzed in detail.•Exploring optimal federated benefit interval through multiple scale experiments.