Deep-Learning Prediction Model with Serial Two-Level Decomposition Based on Bayesian Optimization. (14th September 2020)

• Record Type: Journal Article
• Authors: Jin, Xue-Bo; Wang, Hong-Xing; Wang, Xiao-Yi; Bai, Yu-Ting; Su, Ting-Li; Kong, Jian-Lei
• Other Names: Li Kang Academic Editor.
• Is Part Of: Complexity. Volume 2020(2020)
• Publication Date: 2020-09-14
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2020/4346803
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : The power load prediction is significant in a sustainable power system, which is the key to the energy system's economic operation. An accurate prediction of the power load can provide a reliable decision for power system planning. However, it is challenging to predict the power load with a single model, especially for multistep prediction, because the time series load data have multiple periods. This paper presents a deep hybrid model with a serial two‐level decomposition structure. First, the power load data are decomposed into components; then, the gated recurrent unit (GRU) network, with the Bayesian optimization parameters, is used as the subpredictor for each component. Last, the predictions of different components are fused to achieve the final predictions. The power load data of American Electric Power (AEP) were used to verify the proposed predictor. The results showed that the proposed prediction method could effectively improve the accuracy of power load prediction.