电力系统自动化2016，Vol.40Issue(12)：45-52,8.DOI:10.7500/AEPS20150518007
考虑数据新鲜度和交叉熵的电动汽车短期充电负荷预测模型
Forecasting Model of Short-term EV Charging Load Based on Data Freshness and Cross Entropy
摘要
Abstract
Short-term load forecasting methods for the bus charging station are studied prior to proposing a combined forecasting model based on data freshness and cross entropy.First,the load characteristics are analyzed to show the daily charging load has the features of large fluctuation,periodicity,and being closely related to meteorological conditions(including temperature and rainfall).Secondly,in the accumulation process of historical prediction errors,the combined forecasting model is improved in the following aspects:①Considering the time characteristics and fluctuating characteristics of the charging load sample data,the selecting method of similar days based on grey relational degree is proposed;②Considering the precision and stability of a single method,a combined forecasting model based on cross entropy and normal distribution probability density function is developed to dynamically adj ust the weight coefficients;③Considering the time effectiveness of the data source,the concept of freshness function is put forward,which improves the probability density distribution function of the single forecasting method to further optimize the weight coefficients of the combined forecasting model,improving the accuracy of the model.Finally,the training samples and test samples based on the historical data of a Beijing bus charging station are developed.Compared with single models and other combined forecasting methods,the validity of the combined forecasting model proposed is proved.关键词
电动汽车/负荷预测/交叉熵/新鲜度函数/数据有效性/权重优化Key words
electric vehicle (EV)/load forecasting/cross entropy/freshness function/data validity/weight optimization引用本文复制引用
刘文霞,龙日尚,徐晓波,张建华..考虑数据新鲜度和交叉熵的电动汽车短期充电负荷预测模型[J].电力系统自动化,2016,40(12):45-52,8.基金项目
国家科技支撑计划资助项目(2013BAA02B02)。@@@@This work is supported by National Key Technologies R&D Program(No.2013BAA02B02) （2013BAA02B02）
