Authorized Patent: Clustering Enabled Few-shot load forecasting method and its device

Abstract

The application discloses the scarce sample load forecasting method and prediction device based on clustering. The forecasting method includes feature extraction of historical power load and target samples to obtain feature vector; According to the feature vector, the historical data and the target sample are ensemble-clustered to obtain a stable result. The wavelet denoising algorithm was used to de-noise the clustering results, and the data after denoising were averaged to obtain the time series data of preset length. The time-series data of preset length includes historical time series data and time-series data to be forecasted. The time-series data with preset lengths are input into the two-phase LSTM neural network to obtain the forecasting results of power load. The historical sequence is used to train the second-order LTSM neural network, and the target data is used to adjust the two-phase LTSM neural network in novel tasks. This application still has excellent forecast performance under the condition of a limited training set.

Qiyuan Wang

Ph.D. Student Majoring in Statistics

My research interests include Time Series Clustering, Bayesian Analysis and Federated Learning.