Deep-Learning Prediction Model with Serial Two-Level Decomposition Based on Bayesian Optimization

Autor: Xue-Bo Jin, Hong-Xing Wang, Xiao-Yi Wang, Yu-Ting Bai, Ting-Li Su, Jian-Lei Kong
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: Complexity, Vol 2020 (2020)
Druh dokumentu: article
ISSN: 1076-2787
1099-0526
DOI: 10.1155/2020/4346803
Popis: 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.
Databáze: Directory of Open Access Journals
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