- freely available
Sustainability 2018, 10(1), 118; https://doi.org/10.3390/su10010118
2. Basic Theory
- Determine the number of decomposed IMFs and the number of decompositions
- Add Gaussian white noise sequence to the input signals
- Normalize the signals after adding the white noise sequence
- Decompose the normalized signals to obtain multiple IMF components and one surplus variable:
2.3. Optimized CS Algorithm
- The number of eggs produced by a cuckoo per time is 1.
- The host bird’s nest where high-quality eggs are located is the optimal solution and will be retained for the next generation.
- The number of host nests is certain, and the probability that cuckoo eggs are found by nest owners is .
3. Substation Project Cost Prediction Model Based on EEMD-GCS-SVM
- Decompose the substation cost data to obtain the IMF components and surplus variables through the EEMD method, and normalize the data.
- Initialize the parameters and kernel functions of SVM model, input the normalized decomposed variables into SVM model, and find and determine the optimal parameters and kernel function of SVM model by using GCS algorithm. In order to search for the best parameters of the prediction model faster, the range of c, are set as [0.01, 100], [0.01, 100], respectively. Then, train the prediction model by plugging the historical data into the model and search the best parameter by using the GCS Algorithm. Firstly, set the Nnest (number of birds’ nest) as 20, while Pa (probability of bird’s eggs by bird’s nest owner) is 0.45, and N (number of iterations) is 200. After that, randomly generate Nnest bird nest location W = (W1, W2, ..., Nnest)T. Each bird nest Wi has s parameters (s = the number of weights between input layer and hidden layer + the number of weights between hidden layer and output layer + the number of translation factors + the number of expansion factors). The predicted values of each bird’s nest were calculated, and the nest which has the smallest error in the 20 nests is found, marked as Wbest. Then Wbest retain to the next generation.
- Train the SVM model by using the training set, and then input the test set data to obtain the predictive value of the cost data.
4. Example Analysis
4.1. Basic Data
4.2. Results Analysis and Comparison
- The EEMD-GCS-SVM model established in this paper can effectively improve the prediction accuracy of substation project cost with a MAPE value of only 0.13%, which is much better than that of the un-optimized and EEMD models.
- EEMD method can decompose irregular and non-stationary sequence signals into multiple IMF components and surplus components. The decomposed signals show regularity and periodicity obviously, which improves the prediction accuracy of the model.
- On the basis of CS optimized SVM parameters and kernel function, adding Gauss perturbation can effectively improve the search vitality and range of CS algorithm. The optimal SVM parameters are obtained, the calculation of kernel function is faster, and the computational efficiency and prediction accuracy is improved in the model.
Conflicts of Interest
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|Serial Number||Cost (Yuan/kV·A)||Serial Number||Cost (Yuan/kV·A)||Serial Number||Cost (Yuan/kV·A)||Serial Number||Cost (Yuan/kV·A)|
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