期刊
COMPLEXITY
卷 2020, 期 -, 页码 -出版社
WILEY-HINDAWI
DOI: 10.1155/2020/4346803
关键词
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资金
- National Natural Science Foundation of China [61673002, 61903009, 61903008]
- Beijing Municipal Education Commission [KM201910011010, KM201810011005]
- Young Teacher Research Foundation Project of BTBU [QNJJ2020-26]
- Beijing Excellent Talent Training Support Project for Young Top-Notch Team [2018000026833TD01]
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.
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