A frequency item mining based energy consumption prediction method for electric bus - Zhao, Li; Ke, Hanchen; Huo, Weiwei (Energy (Oxford), 2023)
For a data-driven bus line energy consumption prediction model, building it with statistical indicators on some variables appeared in the whole bus route, such as average speed, maximum acceleration, etc., always decreases its prediction accuracy due to the discarding of the hidden information in the variable change process. To deal with this problem, a frequency item mining based energy consumption prediction method was proposed, in which the useful prediction information hidden in the process of change is mined by frequency item statistics algorithm and stepwise regression algorithm is used to find the optimal combination of input variables. Simulation and experimental analysis show that with multi-dimensions frequency items, the proposed algorithm can describe and reflect the correlation between different input variables appeared in the process. At the same time, a lot of hardware and software computing costs are saved. Replacing procedural input with frequent item can improve learner's accuracy. Frequent item technique can mine the hidden distribution information. Correlations between input variables can be mined by frequency terms. Frequent item can be used in many energy consumption prediction models.