Study On Evaluation Of Electricity Status And Load Forecasting

In recent years,Baotou City has developed rapidly and the development of electric power is facing new challenges.Therefore,this paper aims at Baotou City to carry out the assessment of power status and load forecast,in order to provide a scientific basis for energy use of Baotou City.First,using the Cloud Model,the Internal Structure and the Logarithmic Mean Divisia Index to evaluate the status of electricity.Based on the time horizon,the E_x index of the Cloud Model shows that the summer and autumn are the period of the higher power consumption in first industries and in the rural areas.The winter and spring are the period of the higher power consumption in the second industry,the tertiary industry and the urban.The distribution of the cloud shows that the electricity consumption of the primary industry,the secondary industry and the countryside in 2015 is more uneven and unstable than that in 2016,and the distribution of the electricity consumption of the tertiary industry and the towns in 2015 is equal to2016.The similarity of electricity consumption is uniform and stable.Based on the industrial structure,the internal structure shows that the consumption of industrial electricity(including primary industry,secondary industry and tertiary industry)is greater than that of domestic electricity consumption(including towns and villages).The main contributors to industrial electricity are secondary industry.The main contributors to domestic electricity consumption are towns.The LMDI analysis shows that the production effect is positive,indicating that the economic development has promoted the industrial power consumption,the structural efficiency and the intensity benefit are negative,which indicates that the industrial structure changes inhibit the electricity consumption,and the improvement of electricity efficiency reduces the electricity consumption.Second,Mann-Kendall shows that the overall electricity consumption in Baotou City is on the rise.Finally,the time series and Elman neural network are used to predict the power load.The power load is predicted in the second half of 2017.Although the optimal error precision of time series and Elman neural network is similar,Elman neural network is more stable than that in non-linear power system,and Elman neural network fully expresses the nonlinear variation of the power system characteristics.