Abstract: Traditionally, days before the forecast day are usually selected as historical similar days for training the forecasting model to forecast bus load, without considering the effects of weather situation, weekday and vacation information. Therefore, traditional methods will cause differences of daily characteristics between historical similar days and the forecast day. To solve the problem, a new bus load forecasting method based on Hierarchical Clustering (HC) and Extreme Learning Machine (ELM) was proposed. Firstly, HC method was used for clustering the historical daily bus load. Secondly, a decision tree based on the clustering results was constructed. Thirdly, according to the properties of the forecast day, such as temperature, humidity, weekday and vacation information, historical daily bus load was obtained to train the forecasting model of extreme learning machine through the decision tree. Finally, the forecasting model was established to predict the bus load. When forecasting load of two different buses, compared with traditional single ELM, the proposed algorithm decreases the Mean Absolute Percentage Error (MAPE) by 1.4 percentage points and 0.8 percentage points. The experimental results show that the proposed method has higher accuracy and better stability for forecasting short-term bus load.

Key words: bus load, short-term forecast, Hierarchical Clustering (HC), decision tree, Extreme Learning Machine (ELM)

摘要： 利用传统方法预测母线负荷时，通常选取离待测日相近的一段时间作为历史相似日进行模型训练，没有考虑其天气情况、星期类型、节假日等因素的影响，相似日与待测日特征相差较大。为解决以上问题，提出一种基于层次聚类（HC）和极限学习机（ELM）的母线负荷预测算法。首先使用层次聚类法将母线历史日负荷进行聚类，然后对层次聚类得出的聚类结果建立决策树，其次根据待测日的温度、湿度、星期和节假日类型等日属性在决策树中匹配出训练极限学习机预测模型的历史日负荷，最后建立极限学习机预测模型，对待测日母线日负荷进行预测。对两条不同母线的负荷进行了预测，与传统单一的极限学习机相比，所提算法的平均绝对百分比误差（MAPE）分别降低了1.4和0.8个百分点。实验结果表明，所提算法预测母线负荷具有更高的预测精度和稳定性。

关键词: 母线负荷, 短期预测, 层次聚类, 决策树, 极限学习机