Abstract
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.
Highlights
In recent years, the regional natural gas industry has developed rapidly, and there are factors within the natural gas flow that cause the collapse of the natural gas flow, such as a weak natural gas flow grid and an increase in parallel natural gas containers [1]
Relying on empirical factors will inevitably reduce the scientific nature of the forecast. e method of applying technical means to extract interesting knowledge and information from a large database can process a large amount of historical data accumulated for a long time in the gas daily load and dig out the important influencing factors of the city gas daily load hidden behind the data. e factors affecting the daily load are analyzed quantitatively and qualitatively, so as to grasp the essential characteristics of the daily load of city gas [2–4]
Accidents that caused the collapse of the system due to the instability of natural gas flow have occurred many times in some large natural gas flows abroad, causing long-term and large-scale natural gas shutdowns and huge economic losses. e collapse of the natural gas flow of the system is often caused by the instability of the natural gas flow of a certain busbar or a certain area and spread to the entire system, leading to the collapse of the system. erefore, how to accurately and quickly determine the weak nodes or weak areas where the natural gas flow of the system is stable has become a concern of the majority of researchers [5–7]
Summary
The regional natural gas industry has developed rapidly, and there are factors within the natural gas flow that cause the collapse of the natural gas flow, such as a weak natural gas flow grid and an increase in parallel natural gas containers [1]. E distance from the operating point of the system to the point of collapse of natural gas flow has a linear relationship with the size of the margin index: the influence of various factors such as constraint conditions, natural gas generator active power distribution, and daily load growth methods during the transition process can be taken into account more conveniently. This paper applies the results obtained by the state-space natural gas flow stability weak node partitioning method to the data node system and a certain actual natural gas flow. Is verifies the validity and accuracy of the state-space method proposed in this paper and provides a good practical tool for the analysis of weak nodes and weak regions of natural gas flow stability in the natural gas system. The principal component analysis of classification attributes has more principal components than the comprehensive attributes, the relative mass attributes have been greatly reduced, and it can improve accuracy, save memory, and provide new ideas for studying the stability of online natural gas flow [23–25]
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