A Forecast Model of City Natural Gas Daily Load Based on Data Mining

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.

Wind Power Forecasting Based on LSTM Improved by EMD-PCA-RF

“…EMD, as a data-driven adaptive nonlinear time-varying signal decomposition method, is based on Fourier analysis and Wavelet analysis, and is applicable for smoothing nonlinear non-stationary signals in a step-by-step process. The filtering process of the EMD algorithm decomposes complex time series data into a finite number of Intrinsic Mode Functions (IMFs); these decomposed IMFs contain the fluctuating information of the original data on different time scales [54]. The process is as follows.…”