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An effective dimensionality reduction approach for short-term load forecasting
Yang, Yang Wang, Zijin Gao, Yuchao Wu, Jinran Zhao, Shangrui Ding, Zhe
Yang, Yang
Wang, Zijin
Gao, Yuchao
Wu, Jinran
Zhao, Shangrui
Ding, Zhe
Abstract
Accurate power load forecasting has a significant effect on a smart grid by ensuring effective supply and dispatching of power. However, electric load data generally possesses the characteristics of nonlinearity, periodicity, and seasonality. For complex electric load systems, the presence of redundant information potentially reduces the real pattern extraction for load forecasting. Bearing in mind these issues, we propose an effective forecasting model in which a feature extraction module is introduced that is combined with the variational mode decomposition (VMD) with the variational autoencoder (VAE). In this combination, VMD is utilized for decomposing complex load series and VAE is used to filter the redundant information (noises) from each decomposed series. With two real data sets from China, we demonstrate that the proposed model can achieve highly accurate predictions, as we find the mean absolute percentage error (MAPE) values for one-step-ahead prediction to be 1% (Nanjing) and 0.8% (Taixing), respectively.
Keywords
deep learning, decomposition-ensemble method, feature extraction, load forecasting
Date
2022
Type
Journal article
Journal
Electric Power Systems Research
Book
Volume
210
Issue
Page Range
1-12
Article Number
Article 108150
ACU Department
Institute for Positive Psychology and Education
Faculty of Education and Arts
Faculty of Education and Arts
Relation URI
Source URL
Event URL
Open Access Status
Published as green open access
License
File Access
Controlled
Open
Open