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.

中文翻译：

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基于贝叶斯优化的串行两级分解深度学习预测模型

电力负荷预测在可持续电力系统中意义重大，这是能源系统经济运行的关键。电力负荷的准确预测可以为电力系统规划提供可靠的决策。但是，用一个模型来预测功率负载是一个挑战，尤其是对于多步预测而言，因为时间序列负载数据具有多个周期。本文提出了一种具有串行二级分解结构的深度混合模型。首先，将电力负载数据分解为组件；然后，将具有贝叶斯优化参数的门控循环单元（GRU）网络用作每个组件的子预测器。最后，融合不同组成部分的预测以实现最终预测。美国电力公司（AEP）的电力负荷数据用于验证所提出的预测变量。结果表明，所提出的预测方法可以有效提高电力负荷预测的准确性。