电机与控制应用2024，Vol.51Issue(3)：108-115,后插1,9.DOI:10.12177/emca.2023.198
基于KLPP-K-means-BiLSTM的台区短期电力负荷预测
KLPP-K-means-BiLSTM Based Short-Term Power Load Forecasting for Station Areas
摘要
Abstract
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 analysed with other classical forecasting methods.The proposed method is more suitable for the actual load direction and effectively improves the prediction effect.关键词
电力负荷预测/降维/K均值聚类算法/双向长短时记忆网络/核局部保持投影Key words
power load forecasting/dimensionality/K-means clustering algorithm/bi-directional long short-term memory networks/kernel local preservation projection分类
信息技术与安全科学引用本文复制引用
朱江,汪帆,曹春堂,易灵芝,邹嘉乐..基于KLPP-K-means-BiLSTM的台区短期电力负荷预测[J].电机与控制应用,2024,51(3):108-115,后插1,9.基金项目
国家自然科学基金项目(61572416) （61572416）
湖南省自然科学基金项目(2022JJ50132)National Natural Science Foundation of China(61572416) （2022JJ50132）
Natural Science Foundation of Hu'nan Province,China(2022JJ50132) （2022JJ50132）
