Data mining technology is more and more widely used in the daily load forecasting of natural gas systems. It is still difficult to carry out high-precision, timely intraday load forecasting and intraday load dynamic characteristics clustering for natural gas systems. Based on data mining technology, this paper proposes a stable intraday load forecasting method for the natural gas flow state-space model. The load sensitivity under the current operating conditions of the system is obtained by calculation; the sample space of the state space is established through data processing; the partitions under different clustering radii are calculated; and the best intraday load flow is obtained through the state space effectiveness evaluation method. The experimental results show that the model load forecasting accuracy and relative error reached 98.5% and 0.026, respectively, which solved the problem of processing the long-term accumulated historical data of gas intra-day load. At the same time, the amount of data calculation was reduced by 33.6%, which effectively promoted the quantification of intraday load influencing factors and qualitative analysis.
Since the reform and opening up, there has been a great demand vacancy in the market. The development of green finance can just meet many market demands. Let it stand out among many financing schemes. As an innovative financial model that can effectively promote the global economy, green finance is of great significance to the global ecological economy and environmental state. In this article, we will discuss the economic coupling relationship between environmental quality and green finance. Because we lack a relatively perfect economic evaluation system, therefore we integrate green finance and environmental quality indicators, introduce them into relevant and suitable evaluation system indicators, and mainly analyze and judge based on the grey relational analysis model. The results show the following: (1) investigate the environmental quality and green financial comprehensive benefit development index of A, B, and C cities, judge the lagging types of the three cities, and evaluate the specific development status of the cities. (2) Collect data and calculate the coupling degree and coordination degree of cities. Their coupling levels and coordination types are summarized, respectively. (3) The higher the correlation between urban environmental quality and green financial indicators, the closer the relationship between the two indicators and the greater the mutual influence. (4) Sort them by grey relational degree. The correlation between Y1 and Z3 is the least, which is 0.55 and 0.552, respectively. Y3 can be correlated up to 0.655. The results of this paper are very good, and we get the result that green finance can promote the effective improvement of environmental quality.
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