In order to refine the power dispatching plan, a load situational awareness method is proposed for the bus in the basis of the kmeans clustering and fuzzy neural networks. Firstly, the concept for the static dynamic potential of bus load is proposed. It characterizes the bus load state parameter and the trend of its state parameter change, and then the bus load situational awareness method is established. This method collects and processes the historical load situation information of the bus in the situational awareness stage. In the situation understanding stage, it adopts the kmeans clustering algorithm based on the elbow method which clusters the historical load situation information of the busbar considering the bus environmental factors and load factors. In the situation prediction stage, the Fisher discriminant analysis is utilized to classify the dynamic information of the day to be measured and predict its category of historical data clustering. Then, the historical static potential data of the category is substituted into the fuzzy neural network prediction model to predict the situation of the perceived daily bus load. Finally, a simulation is included to verify the effectiveness and feasibility of the proposed method. It is shown that comparing with the traditional fuzzy neural network prediction, the proposed bus load situational awareness method has the higher situation prediction accuracy.