基于KLPP-K-means-BiLSTM的台区短期电力负荷预测

随着智能电网的发展，各场景的用电更加多元化，而准确的台区负荷预测是确保相关电力部门制定合适检修任务的关键，同时为有序用电、电网经济运行提供重要参考。为了挖掘台区负荷的特征以提高台区负荷预测的精度，提出了一种基于核主元分析与局部保持投影降维、K均值聚类算法(K-means)以及双向长短时记忆网络(BiLSTM)的台区电力负荷预测方法。首先利用核局部保持投影（KLPP）对台区多特征负荷数据进行降维以提取主要特征信息；然后采取K-means聚类算法将相似特征的数据归类成各自的簇集；最后针对聚类后的各典型类型，有针对性地训练BiLSTM，并选取中国某高校低压台区负荷作为算例与其他经典预测方法进行对比分析，结果表明所提方法更拟合实际负荷走向，有效提升了预测效果。

KLPP-K-means-BiLSTM Based Short-Term Power Load Forecasting for Station Areas

With the development of smart grid, the power consumption of each scenario becomes more diversified, and accurate station load forecasting is the key to ensure that the relevant power sector to develop appropriate maintenance tasks, while providing an important reference for orderly power consumption and economic operation. In order to mine the characteristics of the station load to improve the accuracy of the station load forecasting, a station power load forecasting based on the kernel principal components analysis combined with local preservation projection for dimensionality reduction, K-means clustering algorithm (K-means), and bi-directional long short-term memory network (BiLSTM) is proposed. Firstly, the kernel local preservation projection (KLPP) is used to reduce the dimensionality of multi-featured load data in the station area to extract the main feature information. Secondly, the K-means clustering method is adopted to classify the data with similar features into their respective cluster sets. Finally, for each typical type after clustering, BiLSTM is trained in a targeted way, and the load of a low-voltage station area of a university in China is selected as an example to be compared and analyzed with other classical forecasting methods. The proposed method is more suitable for the actual load direction and effectively improves the prediction effect.