融合LSTM和SVM的钢铁企业电力负荷短期预测

为了解决大型钢铁企业电力用电对地区负荷冲击大,电力负荷短期预测准确率低的问题,提出一种融合长短期记忆网络(long short-term memory,LSTM)和支持向量机(support vector machine,SVM)的负荷短期预测算法.对钢铁工业地区负荷特性进行分析,根据系统负荷的组成部分将负荷细分为冲击...

Short-term load forecasting of iron and steel industry area based on combination model of SVM and LSTM

A short-term load forecasting algorithm combining long short-term memory (LSTM) and support vector machine (SVM) was proposed to solve the low accuracy problem of short-term load forecasting due to the large-scale iron and steel enterprise power consumption impact on regional load. The research thoroughly analyzed the load characteristics of the selected region with predominant iron and steel mill load, which divided the load into the impulse load and others based on its various components.Covariance algorithm and Pearson algorithm were used to analyze the correlation and differentiation of load influence factors. Six attributes of historical load, temperature, date type, steel price, electricity price and iron ore price were selected as load forecasting. The fuzzy weight assignment was used to fuse LSTM and SVM which got the final load forecasting result. The simulation results showed that the proposed method could predict the short-term load more accurately than the single LSTM or SVM.