The Bus Load Forecasting Of Regional Power Grid Based On The Intelligent Processing Of Multi Meteorological Factors

With the continuous development of society and science technology, the management of power system and grid is becoming more and more complex, modern and perfect. The load forecasting of electric power system has become an important research in modern power system science, especially the bus load forecasting results, the accuracy of which will have a significant impact on safety check and results of the prior plan. Therefore, it’s an important measure to improve the operation of the power grid power grid to actively carry out the bus load forecasting work, accurately study the change of bus load and improve the accuracy of forecasting. At the same time, the increasing use of heating and refrigeration equipment make the power load influenced by the meteorological factors, and the meteorological factors gradually become the focus of the research on the quantitative impact of the power load.This paper studies the affection of meteorological factors on the bus load forecasting based on the actual situation of the regional power grid and 220kV substation and analyzes the single meteorological factor, the multi meteorological factors, the day-characteristics meteorological factors, the real time meteorological factors and the cumulative benefits. Among the single meteorological factors, it made a correlation analysis between the 10 factors, such as air pressure, sea level, average temperature, the highest temperature, the lowest temperature, relative humidity, the maximum wind speed, the 10 minutes maximum wind speed, and the 10 minute rainfall and visibility, and the bus load. Based on the correlation analysis, combined with the existing power load forecasting methods, a new method of 220kV substation bus load forecasting is proposed. The Intelligent prediction algorithm base is built, which includes some classical prediction algorithms, such as a single exponential smoothing method, and some optimization algorithms, such as BP algorithm and RBF algorithm. Finally, according to the characteristics of these prediction algorithms, the optimization scheme is formulated. At last, under the condition of the meteorological big data, combining with the vector of day-characteristics meteorological factors and the method of identifying the weather mutation with the fuzzy threshold, the paper studies the influences of temperature, humidity, wind speed, precipitation, air pressure and other meteorological factors on the bus load, proposing the methods of bus load forecasting considering the weather mutation.This paper chooses the city of Nanjing in Jiangsu Province as an example of the regional power grid, and chooses the Zhongyangmen substation as an example of the 220kV substation. It collects the raw data of the meteorological factors and the corresponding dates between June 1, to July 27 2014, and analyzes the above theories to verify its scientific and advanced nature.