# Research on Substation Project Cost Prediction Based on Sparrow Search Algorithm Optimized BP Neural Network

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## Abstract

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## 1. Introduction

- (1)
- Starting from the concept of data space, this paper uses the data of the whole life cycle of substation engineering to comprehensively investigate the factors of substation engineering cost and the index library of substation project cost factors based on technical factors is constructed after secondary screening.
- (2)
- For the first time, the sparrow search algorithm is used to optimize a BP neural network for the prediction of substation project cost. Based on model input indexes, the SSA is used to optimize the weights and thresholds of a BP neural network, so as to construct an SSA-BP prediction model to predict the substation project cost.

## 2. Research on Influencing Factors Identification of Substation Project Cost Based on Data Space

#### 2.1. Data Space

- (1)
- Object-oriented as the main body. In the past, the object-oriented data management was business-oriented, but nowadays the data space is organized and integrated for the data needed by the subject and necessary.
- (2)
- Consider the whole life cycle. Big data and database technologies often cannot consider the whole life cycle of data, and the data space can be described as a data store with multiple labels for the whole life cycle of objects.
- (3)
- Effectively break the data barriers. One of the main reasons for the complexity and low efficiency of enterprise work is that the data barrier is not broken. The emergence of the concept of data space can be used as a powerful weapon to break the “isolated data island”, so that all levels of departments can use relevant data uniformly, thus, improving work efficiency and decision-making level.

#### 2.2. Construction of Primary Selection Library for Influencing Factors of Substation Project Cost Prediction

#### 2.3. Grey Relation Analysis

- (1)
- Determination of the evaluation index system according to evaluation purpose. Collecting evaluation data and determining the parent series and subseries; the parent series being the target series and the subseries being the series consisting of the relevant influencing factors.
- (2)
- Dimensionless processing of sequences. Since the physical significance of each factor is not the same, the data are dimensionless when screening the indicators in order to facilitate comparison and, thus, draw correct conclusions.

- (3)
- Find the absolute difference. Firstly, the absolute difference of the sequence needs to be calculated, as shown in Equation (5). Let be the absolute difference ${Y}_{i0}$ between and ${Y}_{ij}$:

- (4)
- Find the grey relation degree. Using an arithmetic average method to calculate grey relation degree, as shown in Equation (9):

- (5)
- Ranking the correlation degree. The calculated correlation ${r}_{j}$ is sorted from large to small or from small to large, and the analysis results are obtained.

## 3. Intelligent Prediction Model of Substation Project Cost Based on Sparrow Search Algorithm Optimized BP Neural Network

#### 3.1. BP Neural Network

#### 3.2. Basic Principle of Sparrow Search Algorithm

- (1)
- As discoverers, searching for food.
- (2)
- As joiners, using the finders to obtain food.
- (3)
- As scouts, finding danger to decide whether the group continues to forage.

#### 3.3. SSA-BP Prediction Model

- (1)
- Data preprocessing. Including dividing the training and test sets and normalizing the data.
- (2)
- Determine the BPNN topology. The nodes of input layer and output layer are obtained by size function, and the determination of hidden nodes uses the cycle process, the minimum error in the cycle process corresponds to the optimal hidden layer node.
- (3)
- Initialize BPNN weights and thresholds.
- (4)
- The SSA is used to seek the optimal value and threshold. It includes calculating population fitness, foraging behavior, and anti-predator behavior.
- (5)
- Output BP neural network optimal parameters.
- (6)
- Get the optimal parameters of the model for instance prediction.

## 4. Case Analysis

#### 4.1. Select Samples

#### 4.2. Determine Model Input Indexes

#### 4.3. Prediction Results and Comparative Analysis

- (1)
- Process Data. There are four methods of data dimensionless: normalization, regularization, standardization, and centralization. Since the normalized data has the advantage of improving the rate of convergence and accuracy of the model, this article selects the normalization method to conduct dimensionless processing of the data. The normalization method is also called deviation standardization, which maps the data to a specific interval [0,1] after the linear conversion of the original data. The conversion function is shown in formula (3).
- (2)
- Determine the structure and initialize the parameters. According to the above analysis, the selected 20 impact factors are used as input indexes, and the static total investment of substation is used as output indicators to build the model. BP and SSA parameter settings are shown in Table 3 below.
- (3)
- Network training and result analysis. Through learning and training of historical engineering data, 30 of them are used as test sets to validate the effectiveness of the SSA-BP prediction model. The results of real value and predicted value and the relative error between them are shown in Table 4. For a more visual observation of the results, a variety of error evaluation indexes are used to compare the results, as follows Equations (13)–(16):

## 5. Conclusions

- (1)
- Based on the concept of data space, this paper takes the substation project as the main body of space and uses the whole life cycle data, such as researchable estimation, preliminary design budget, and construction drawing budget, to mine the input factors of the prediction model. It establishes 20 technical factors including construction cost, installation cost, equipment purchase cost, and other costs, which covers a wide range and reduces the redundancy index.
- (2)
- In this paper, the SSA is used to optimize the weights and thresholds of a BP neural network, thus predicting the cost of substation engineering. The prediction results show that compared with the unoptimized BP neural network model and the WOA-BP and PSO-BP prediction models, the SSA-BP intelligent prediction model proposed in this paper has better prediction accuracy and can be used for actual prediction.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Nomenclature

BPNN | Back Propagation Neural Network |

SSA | Sparrow Search Algorithm |

SVM | Support Vector Machine |

PCA | Principal Component Analysis |

PSO | Particle Swarm Optimization |

APSO | Adaptive Particle Swarm Optimization |

ELM | Extreme Learning Machine |

LSSVM | Least Squares Support Vector Machine |

GRA | Grey Relation Analysis |

SVR | Support Vector Regression |

WOA | Whale Optimization Algorithm |

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Index | Variable Attributes | Index | Variable Attributes |
---|---|---|---|

X1 | Quantization with/without adjustable load pressure | X12 | Single unit capacity of high voltage reactor |

X2 | Number of current period stations(three-phase) | X13 | Number of low voltage capacitors |

X3 | Number of long-term stations(three-phase) | X14 | Number of low voltage reactors |

X4 | Single capacity(three-phase) | X15 | Number of control cables |

X5 | High voltage side distribution device type | X16 | Average unit price of control cable |

X6 | Number of high voltage side circuit breakers | X17 | Number of power cables 1 kV and below |

X7 | Medium voltage side distribution device type | X18 | Average unit price of power cables 1 kV and below |

X8 | Number of medium voltage side circuit breakers | X19 | Length of optical cable |

X9 | Low voltage side distribution device type | X20 | Amount of grounding material flat steel used |

X10 | Number of low voltage side circuit breakers | X21 | Amount of copper row for grounding materials |

X11 | Number of high voltage reactors |

Input Category | Input Indexes | Input Category | Input Indexes |
---|---|---|---|

Construction engineering factors | Main control building area | Installation engineering factors | Single capacity(three-phase) |

Number of high voltage side intervals | Number of low voltage side circuit breakers | ||

Number of medium voltage side intervals | Number of control cables | ||

Main transformer and line steel quantity and bracket quantity | Average unit price of control cable | ||

Main transformer and concrete quantity of line foundation | Average unit price of power cables 1 kV and below | ||

Site leveling costs | Equipment purchase factors | Number of main transformers | |

Retaining wall and slope protection costs | Secondary equipment | ||

The method of foundation treatment | Other factors | Total construction costs | |

Out-of-station water costs | Project construction management costs | ||

Out-of-station power costs | Total project construction technical service costs |

Model | Parameter | Value | Model | Parameter | Value |
---|---|---|---|---|---|

BPNN | Training frequency | 1000 | SSA-BP | Initial population scale | 30 |

Learning rate | 0.01 | Evolutional times | 50 | ||

Number of input layer nodes | 20 | Proportion of discoverers | 0.7 | ||

Optimal hidden layer node | 14 | Proportion of scouts | 0.2 | ||

Number of output layer nodes | 1 | Safety values | 0.6 |

Project Number | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
---|---|---|---|---|---|---|---|---|---|---|

Actual value | 2638 | 3592 | 2609 | 4936 | 4507 | 3570 | 3815 | 1647 | 11,371 | 12,708 |

Predicted value | 2829 | 3780 | 2868 | 4820 | 4279 | 3249 | 3276 | 1762 | 10,402 | 11,892 |

Error (%) | 7.24 | 5.22 | 9.94 | 2.35 | 5.06 | 8.99 | 14.12 | 6.95 | 8.53 | 6.42 |

Project number | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 |

Actual value | 9139 | 9961 | 10,484 | 9727 | 4119 | 3305 | 3538 | 2668 | 10,647 | 11,662 |

Predicted value | 8585 | 9772 | 10,277 | 10,703 | 4139 | 3034 | 3215 | 2680 | 10,766 | 11,808 |

Error (%) | 6.07 | 1.90 | 1.97 | 10.04 | 0.49 | 8.19 | 9.13 | 0.46 | 1.11 | 1.26 |

Project number | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 |

Actual value | 11,316 | 14,362 | 3047 | 3310 | 3142 | 3535 | 3275 | 3248 | 3405 | 3155 |

Predicted value | 11,427 | 14,303 | 3304 | 3282 | 3196 | 3531 | 3524 | 3122 | 3230 | 3180 |

Error (%) | 0.98 | 0.41 | 8.45 | 0.86 | 1.71 | 0.11 | 7.61 | 3.87 | 5.15 | 0.79 |

Parameter Setting | |
---|---|

Epoch | 1000 |

Learning rate | 0.01 |

Minimum error of training target | 0.0001 |

Momentum factor | 0.01 |

Gradient | 1 × 10^{−6} |

Initial population size | 30 |

BPNN | PSO-BP | WOA-BP | SSA-BP | |
---|---|---|---|---|

RMSE | 1168.29 | 603.10 | 545.78 | 363.52 |

MAE | 760.48 | 469.28 | 367.18 | 254.94 |

MAPE (%) | 12.63 | 9.51 | 6.56 | 4.85 |

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## Share and Cite

**MDPI and ACS Style**

Xu, X.; Peng, L.; Ji, Z.; Zheng, S.; Tian, Z.; Geng, S.
Research on Substation Project Cost Prediction Based on Sparrow Search Algorithm Optimized BP Neural Network. *Sustainability* **2021**, *13*, 13746.
https://doi.org/10.3390/su132413746

**AMA Style**

Xu X, Peng L, Ji Z, Zheng S, Tian Z, Geng S.
Research on Substation Project Cost Prediction Based on Sparrow Search Algorithm Optimized BP Neural Network. *Sustainability*. 2021; 13(24):13746.
https://doi.org/10.3390/su132413746

**Chicago/Turabian Style**

Xu, Xiaomin, Luyao Peng, Zhengsen Ji, Shipeng Zheng, Zhuxiao Tian, and Shiping Geng.
2021. "Research on Substation Project Cost Prediction Based on Sparrow Search Algorithm Optimized BP Neural Network" *Sustainability* 13, no. 24: 13746.
https://doi.org/10.3390/su132413746