摘要
提出一种基于长短时记忆网络(LSTM)的预测模型,以提高高校电力负荷预测的准确率。以某高校的电力负荷数据为研究对象,分析不同超参数的影响,确定最优的预测模型,并与常用的基于支持向量机(SVM)的负荷预测模型进行负荷预测对比。结果表明,本文提出的负荷预测模型平均绝对百分误差(MAPE)为:办公楼6.67%、科研楼4.32%、教学楼5.98%和宿舍4.57%,每类建筑均比基于SVM预测模型的MAPE低1.5%左右。
A forecast model based on long short term memory(LSTM)is proposed to improve the accuracy of power load forecast in university.Taking the power load data of a university as the research object,this paper analyzes the influence of different hyper-parameters,determines the optimal forecast model,and compares it with the commonly used load forecast model based on support vector machine(SVM).The results show that the mean absolute percentage error(MAPE)of the load forecast model proposed in this paper is 6.67%for office buildings,4.32%for scientific research buildings,5.98%for teaching buildings and 4.57%for dormitories.In addition,each type of building is about 1.5%lower than the MAPE based on SVM forecast model.
作者
满达
张卓凡
张金金
谢将剑
MAN Da;ZHANG Zhuofan;ZHANG Jinjin;XIE Jiangjian(Beijing Forestry University,Beijing 100083,China)
出处
《建筑电气》
2021年第11期58-63,共6页
Building Electricity
关键词
高校建筑
负荷预测
负荷特征
负荷曲线
长短时记忆网络
支持向量机
平均绝对百分误差
实验对比
university buildings
load forecast
load characteristics
load curve
LSTM
support vector machine
mean absolute percentage error
experimental comparison
