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The development of the field of electronic information technology is constantly deepening, which is of great significance to the upgrading of traditional industries and the leap forward in the level of electronic information technology. From small individuals to large countries, information security and technology levels depend on in-depth research and development in the field of electronic information technology. Keeping pace with the times and promoting their further development is the key to promoting the progress of human civilization. This paper uses the time series model to estimate it and provide ideas for its further development.

With a large number of new components and new technologies widely used in power system, the method of power equipment outage probability based on law of large number will no longer be applicable due to the lack of statistical data. This paper considers the high order uncertainty of component failure probability under the condition of small sample size and uses interval values to represent the reliability parameters of the component when carrying out the risk assessment of power system, and propose Belief Universal Generating Function by combining Universal Generating Function with Belief Function Theory. This method can effectively avoid over estimation in interval arithmetic and get a more accurate interval value of power system risk assessment index.

[1] proposed a novel analysis and forecast method of electricity business expansion based on Seasonal Adjustment, we extend this work to include the effect the micro and macro aspects, respectively. From micro aspect, we introduce the concept of load factor to forecast the stable value of electricity consumption of single new consumer after the installation of new capacity of the high-voltage transformer. From macro aspects, considering the growth of business expanding is also stimulated by the growth of electricity sales, it is necessary to analyse the antecedent relationship between business expanding and electricity sales. First, forecast electricity consumption of customer group and release rules of expanding capacity, respectively. Second, contrast the degree of fitting and prediction accuracy to find out the antecedence relationship and analyse the reason. Also, it can be used as a contrast to observe the influence of customer group in different ranges on the prediction precision. Finally, Simulation results indicate that the proposed method is accurate to help determine the value of expanding capacity and electricity consumption.

At present, reliability assessment plays an important role in power systems. When the component failure probabilities are interval valued, common methods fail to achieve reasonable interval reliability assessment result of power systems. In this paper a novel approach based on the belief universal generating function (BUGF) is proposed to calculate the reliability indexes of power systems. Instead of giving a single-valued assessment result, a belief function and a plausibility function are exploited to calculate the lower and upper bounds of the loss of load probability (LOLP), the loss of load expectation (LOLE), the expected unsupplied load (EUL) and the expected unsupplied energy (EUE) in UGF, respectively. The proposed approach can track the correlation of the original data well and keep it to the end of the calculation. By using BUGF compared to other common methods to calculate the interval LOLP, LOLE, EUL, and EUE of IEEE-RTS 79, the results show the BUGF method can track the correlation of the original data well, and can get narrower and more accurate interval reliability indexes of the power generation system, which illustrates the effectiveness of the proposed approach.