基于空间映射区分度提升和Bi-LSTM的PLF  

Power Load Forecasting Based on Spatial Map Differentiation Promotion and Bi-LSTM

作　　者：陈建平 周明 许锋 魏业文[3] CHEN Jian-ping;ZHOU Ming;XU Feng;WEI Ye-wen(Zhejiang Shuangcheng Electric Co.,Ltd,Shaoxing Zhejiang 312000,China;Shengzhou Guangyu Industrial Co.,Ltd,Shengzhou Zhejiang 312000,China;China Three Gorges University College of Electrical Engineering&New Energy,Yichang Hubei 443000,China)

机构地区：[1]浙江双成电气有限公司,浙江绍兴312000 [2]嵊州市光宇实业有限公司,浙江嵊州312000 [3]三峡大学电气与新能源学院,湖北宜昌443000

出　　处：《计算机仿真》2025年第1期47-51,120,共6页Computer Simulation

摘　　要：提出了基于多颜色空间高维映射和Bi-LSTM深度网络相结合的电力负荷预测方法。将图像处理中经典的多颜色空间模型将原始的电力负荷数据映射至高维空间,提升电力负荷数据的空间可分离性,同时对高维数据应用主元分析方法进行降维,确保预测精度的前提下,提升算法的运行效率,应用Bi-LSTM深度网络实现对电力负荷的预测。实验结果表明,基于改进的多颜色空间模型和主元分析方法实现的高维空间映射方法可有效的提升电力负荷数据的空间可分离性,提升了电力负荷预测的精度。A power load forecasting method based on the combination of CN(Color Names)space high-dimensional mapping and Bi-LSTM deep network is proposed.The classic multi-color space model in image processing maps the original power load data to the high-dimensional space to improve the spatial separability of the power load data.At the same time,the principal component analysis method is applied to the high-dimensional data to reduce the dimension.On the premise of ensuring the prediction accuracy,the operation eficiency of the algorithm is improved,and the Bi-LSTM depth network is applied to realize the power load prediction.The experimental results show that the high-dimensional spatial mapping method based on the improved multi-color space model and principal component analysis method can effectively improve the spatial separability of power load data and improve the accuracy of power load forecasting.

关 键 词：电力负荷预测 多颜色空间模型 空间映射区分度 

分 类 号：TM711[电气工程—电力系统及自动化]