关键词: 母线负荷, 短期预测, 层次聚类, 决策树, 极限学习机

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)