Customer Demand Forecast And Countermeasures In Electric Vehicle Conversion Station

In this paper,the example of charging and replacing electric vehicles is studied.The statistical knowledge is used to predict and analyze the electric power demand of electric vehicles,and the power supply service management strategy of conversion station is optimized.Firstly,by analyzing the historical data of electric vehicle charging and replacing power demand,a demand forecasting model is constructed.They are used to forecast monthly demand data and daily demand data,respectively.For monthly demand data,the CensusX-12 seasonal adjustment method in Eviews software is used to eliminate the influence of seasonal variation factors on the accuracy of prediction results under the premise of ensuring the objective variation of variables.Then,through the stationarity test and identification of the model,the power demand forecasting model of the charging and replacing power station constructed in this paper is determined,namely the seasonal autoregressive moving average model ARIMA(4,1,0).Finally,the electricity consumption of the Beijing Gao'an community conversion station between 2014 and 2017 was used as historical data,and the power load of the conversion station 2018 was predicted by the above model.Comparing the predicted value with the actual value,the results show that the relative error is controlled within 0.01%,which verifies the rationality of the monthly electricity demand forecasting model for the power station is verified.;In the forecast of daily demand data,because the short-term forecast does not have the influence of seasonal changes,the exponential smoothing method is used to predict the daily demand to guide the specific operational arrangements of the conversion station.Then,the the electric vehicle conversion station service is regarded as a multi-stage decision-making process.Under the premise of ensuring the demand for electricity,and aiming at reducing the operating cost,the optimization model of the conversion station is constructed.On this basis,the relationship between the cost of replacing a power station and the cost of storage batteries and the cost of supplementing the battery is calculated by using the model.The model can effectively optimize the power exchange service capacityof the conversion station under the premise that the demand for electricity is known.Finally,the the electric vehicle conversion station demand forecasting model and the conversion station service optimization model are combined with actual cases.Taking Beijing Gao'an community conversion station as an example,after analyzing its existing charging and replacing basic equipment and charging and replacing capacity,the power demand of the power station was predicted in 2019.On this basis,select the operating conditions in January for research.Finally,in the operation management strategy of the electric vehicle conversion station,it puts forward its own suggestions,in order to provide certain reference value for the planning and management of the electric vehicle conversion station.