基于内存计算的电力负荷预测

Power Load Forecasting Based on Memory Computing

内存计算技术的提出和发展,是基于实际情况的需求.对诸多行业来说,其在数据处理方面存在各种各样的问题及困难,诸如数据处理量极大、数据处理效率偏低、处理速度慢等,电力行业的负荷预测也遇到阻碍,要对大批量的数据实时分析做出预测成为一大难题,本文就以基于内存计算,结合BP神经网络预测模型,研究负荷预测中的问题,实验证明比传统方法有质的提升.

Proposing memory computing and the development of technology, is based on the actual situation needs. For industries, its data processing is facing all kinds of problems and difficulties, such as large amount of data processing, low efficiency of data processing and slow processing speed, load forecasting of electric power industry also meet obstacles, to large quantities of data real-time analysis make predictions become a big problem, in this paper, the calculation based on memory, combined with bp neural network prediction model, make the research problems on load forecasting, compared with traditional methods, experimental proof has qualitative improvement.