基于RF变量选择与LSTM回归的长期用电量预测模型

Long-term Electricity Consumption Forecast Model Based on RF Variable Selection and LSTM Regression

由于当前长期用电量预测方法难以解决变量选择问题,造成用电量预测结果不准确,为此将随机森林(RF)算法变量选择与长短期记忆(LSTM)网络回归两者结合,设计基于RF变量选择与LSTM回归的长期用电量预测模型。采用RF方法对单一变量的重要性进行评估,获取各项影响因素与用电量之间的相关系数,然后选取其中取值较高的变量作为用电量预测的依据。结合RF变量选择结果,分析动力系统理论,采用收敛交叉映射方法研究用电量与工业发展水平、温度等因素之间的关系,基于各因素之间的关系结合LSTM回归方法,组建用电量预测模型,实现长期用电量预测。研究结果表明,与传统方法相比,所设计模型的用电量预测精度与预测效率较高,能够快速、准确地完成长期用电量预测,表明该模型的应用价值更高。

Existing long-term electricity consumption prediction methods are difficult to solve the problem of variable selection,which leads to inaccurate prediction results of power consumption.Therefore,combining the radio frequency variable selection in random forest(RF)algorithm with long short-term memory(LSTM)regression in long-term and short-term memory networks,a long-term electricity consumption prediction model based on RF variable selection and LSTM regression was designed.RF method was used to evaluate the importance of a single variable,and the correlation coefficients between each influencing factors and electricity consumption were obtained.Then,the variable with higher value was selected as the basis of electricity consumption forecast.Combined with the selection results of RF variables,the theory of power system was analyzed,and the relationship between power consumption and industrial development level,temperature and other factors was studied by using convergence cross mapping method.Based on the relationship between various factors and the LSTM regression method,a prediction model of electricity consumption was established,and the long-term prediction of electricity consumption was realized.The results show that,compared with the traditional methods,the designed model has higher prediction accuracy and efficiency,can predict the long-term electricity consumption in the growing period quickly and accurately,and has high application value.