An integrated federated learning algorithm for short-term load forecasting

Accurate power load forecasting plays an integral role in power systems. To achieve high prediction accuracy, models need to extract effective features from raw data, and the training of models needs a large amount of data. However, data sharing will require the disclosure of the private data of the participants. To address this issue, we combined variational mode decomposition (VMD), the federated k-means clustering algorithm (FK), and SecureBoost into a single algorithm, called VMD-FK-SecureBoost. First, we used VMD to decompose the original data into several sub-sequences. This enabled us to extract the implied features to separately predict each sub-sequence to improve the prediction accuracy. Second, we use FK to recombine the sub-sequences into several clusters with common characteristics. Finally, with SecureBoost, we use clustering results to realize federated learning with privacy protection. We calculated the prediction values by accumulating the prediction results of the sub-sequences. The results for the examples in the US and Australia showed that the prediction performance of VMD-FK-SecureBoost was better than those of XGBoost and SecureBoost. Particularly, the MAPEs of one-step-ahead forecasting in the Texas and Newcastle CBD from our proposed method are 0.209% and 2.127% respectively, which are the lowest of all the algorithms.

Automated summary

Accurate power load forecasting is crucial in power systems, and extracting effective features from raw data and having a large amount of training data is essential for high prediction accuracy. To address the issue of data sharing and privacy, the VMD-FK-SecureBoost algorithm combines variational mode decomposition (VMD), federated k-means clustering algorithm (FK), and SecureBoost. This algorithm utilizes VMD to decompose the data, FK to recombine sub-sequences, and SecureBoost for federated learning with privacy protection. The results showed that VMD-FK-SecureBoost outperformed XGBoost and SecureBoost in power load forecasting, with the lowest MAPEs in the Texas and Newcastle CBD areas.