Cost Forecasting of Substation Projects Based on Cuckoo Search Algorithm and Support Vector Machines

Abstract

Accurate prediction of substation project cost is helpful to improve the investment management and sustainability. It is also directly related to the economy of substation project. Ensemble Empirical Mode Decomposition (EEMD) can decompose variables with non-stationary sequence signals into significant regularity and periodicity, which is helpful in improving the accuracy of prediction model. Adding the Gauss perturbation to the traditional Cuckoo Search (CS) algorithm can improve the searching vigor and precision of CS algorithm. Thus, the parameters and kernel functions of Support Vector Machines (SVM) model are optimized. By comparing the prediction results with other models, this model has higher prediction accuracy.

Cost Forecasting Model of Transformer Substation Projects Based on Data Inconsistency Rate and Modified Deep Convolutional Neural Network

“…Second, optimizing the parameters of the neural network or the support vector machine can provide the training accuracy of the model. For example, the prediction results of References [14] and [36] were superior to the prediction results of the SVM (mentioned in Figure 7). Third, the DCNN model not only reduces the number of neurons and weights, it also uses the pooling operation to make the input features have displacement, scaling and distortion invariance, thus improving the accuracy and robustness of network training, which is better than the SVM and the BPNN.…”

“…Owing to the fact that the DCNN has advantages over shallow learning algorithms, the MFOA was able to complete parameter optimization of the DCNN, and the DIR approach guarantees the completeness of the input information while reducing the redundant data, which ameliorates the prediction accuracy and robustness. For further verification that the proposed method is better, the case was predicted by the methods proposed in Reference [8] (BP neural network), [14] (cuckoo search algorithm and support vector machine), and [36] (modified firefly algorithm and support vector machine). The input of these three models was 33-that is 33 candidate features-and the parameter settings were consistent with those mentioned in the text.…”

“…Table 3 displays the comparative forecasting results. [14] 3.3641 3.4502 3.3122 Ref. [36] 3.2794 3.3471 3.2098…”

“…The larger the sample size of the training set, the better the test results. Due to the limited number of new substation projects each year, when applying the proposed model, it is necessary to collect more data on the cost of the For further verification that the proposed method is better, the case was predicted by the methods proposed in Reference [8] (BP neural network), [14] (cuckoo search algorithm and support vector machine), and [36] (modified firefly algorithm and support vector machine). The input of these three models was 33-that is 33 candidate features-and the parameter settings were consistent with those mentioned in the text.…”

“…Reference [13] investigated an SVM integrated with adaptive particle swarm optimization (APSO) to forecast the cost of a practical substation project. In reference [14], a cuckoo search algorithm (CS) was introduced to optimize the parameters in an SVM. The results showed that the forecasting precision was obviously enhanced.…”