School of Electrical Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Korea.
Sensors (Basel). 2021 Nov 19;21(22):7697. doi: 10.3390/s21227697.
An efficient energy operation strategy for the smart grid requires accurate day-ahead electricity load forecasts with high time resolutions, such as 15 or 30 min. Most high-time resolution electricity load prediction techniques deal with a single output prediction, so their ability to cope with sudden load changes is limited. Multistep-ahead forecasting addresses this problem, but conventional multistep-ahead prediction models suffer from deterioration in prediction performance as the prediction range is expanded. In this paper, we propose a novel two-stage multistep-ahead forecasting model that combines a single-output forecasting model and a multistep-ahead forecasting model to solve the aforementioned problem. In the first stage, we perform a single-output prediction based on recent electricity load data using a light gradient boosting machine with time-series cross-validation, and feed it to the second stage. In the second stage, we construct a multistep-ahead forecasting model that applies an attention mechanism to sequence-to-sequence bidirectional long short-term memory (S2S ATT-BiLSTM). Compared to the single S2S ATT-BiLSTM model, our proposed model achieved improvements of 3.23% and 4.92% in mean absolute percentage error and normalized root mean square error, respectively.
智能电网的高效能源运行策略需要具有高精度时间分辨率的准确日前电力负荷预测，例如 15 分钟或 30 分钟。大多数高时间分辨率电力负荷预测技术都针对单一输出预测，因此其应对突发负荷变化的能力有限。多步预测可以解决这个问题，但传统的多步预测模型随着预测范围的扩大，预测性能会下降。本文提出了一种新颖的两阶段多步预测模型，该模型结合了单输出预测模型和多步预测模型，以解决上述问题。在第一阶段，我们使用带有时间序列交叉验证的轻量级梯度提升机基于最近的电力负荷数据进行单输出预测，并将其输入到第二阶段。在第二阶段，我们构建了一个多步预测模型，该模型将注意力机制应用于序列到序列双向长短期记忆（S2S ATT-BiLSTM）。与单一的 S2S ATT-BiLSTM 模型相比，我们提出的模型在平均绝对百分比误差和归一化均方根误差方面分别提高了 3.23%和 4.92%。
