Research on Prediction Method of Reasonable Cost Level of Transmission Line Project Based on PCA‐LSSVM‐KDE

Abstract

In order to reduce the investment risk, the evaluation standard of transmission line project investment planning becomes higher, which puts forward higher requirements for the reasonable level prediction of transmission line project cost. This paper combines principal component analysis (PCA) with the least squares support vector machine (LSSVM) model and establishes a point prediction model for transmission line project cost. Based on the analysis of the error of the point prediction model, the kernel density estimation (KDE) method is innovatively introduced to estimate the prediction error, and the probability density function of the error is obtained. Then, according to different confidence levels, the corresponding cost intervals are obtained, which means that the reasonable level of transmission line project cost is obtained. The results show that the coverage rate of the cost prediction interval under 85% confidence level is 88.57%. This conclusion shows that the model has high reliability and can provide a reliable basis for the evaluation of transmission line project investment planning.

Multi‐output regression for imbalanced data stream

“…For UK‐DALE dataset, the length of the input vector has been reduced from 240 to 25 after PCA. Figure 6 shows that: (1) when the number of generated dimensions of the inputs exceeds 25, the share of variance explained by the added dimension tends to zero; (2) the share of cumulative explanatory variance of the 25 dimensions is 94%, which exceeds 85% and is therefore expected to explain almost all of the variance (Xue‐Hua et al, 2019). Similarly, for CU‐BEMS, the length of the input vector has been reduced from 66 to 10 after PCA (see Figure 7).…”