Novel PV Power Hybrid Prediction Model Based on FL Co-Training Method

Abstract

Existing photovoltaic (PV) power prediction methods suffer from insufficient data samples, poor model generalization ability, and the inability to share power data. In this paper, a hybrid prediction model based on federated learning (FL) is proposed. To improve communication efficiency and model generalization ability, FL is introduced to combine data from multiple locations without sharing to collaboratively train the prediction model. Furthermore, a hybrid LSTM-BPNN prediction model is designed to improve the accuracy of predictions. LSTM is used to extract important features from the time-series data, and BPNN maps the extracted high-dimensional features to the low-dimensional space and outputs the predicted values. Experiments show that the minimum MAPE of the hybrid prediction model constructed in this paper can reach 1.2%, and the prediction effect is improved by 30% compared with the traditional model. Under the FL mode, the trained prediction model not only improves the prediction accuracy by more than 20% but also has excellent generalization ability in multiple scenarios.

An Accelerator for Semi-Supervised Classification with Granulation Selection

“…Another area of application for co-training is in data privacy, where it is often the case that only a limited amount of labeled data is available for training machine learning models. In these scenarios, co-training can effectively leverage the information contained in the unlabeled data to improve the performance of the classifier, without compromising privacy or security [9].…”