This paper proposes a new power grid investment prediction model based on the deep restricted Boltzmann machine (DRBM) optimized by the Lion algorithm (LA). Firstly, two factors including transmission and distribution price reform (TDPR) and 5G station construction were comprehensively incorporated into the consideration of influencing factors, and the fuzzy threshold method was used to screen out critical influencing factors. Then, the LA was used to optimize the parameters of the DRBM model to improve the model's prediction accuracy, and the model was trained with the selected influencing factors and investment. Finally, the LA-DRBM model was used to predict the investment of a power grid enterprise, and the final prediction result was obtained by modifying the initial result with the modifying factors. The LA-DRBM model compensates for the deficiency of the single model, and greatly improves the investment prediction accuracy of the power grid. In this study, a power grid enterprise was taken as an example to carry out an empirical analysis to prove the validity of the model, and a comparison with the RBM, support vector machine (SVM), back propagation neural network (BPNN), and regression model was conducted to verify the superiority of the model. The conclusion indicates that the proposed model has a strong generalization ability and good robustness, is able to abstract the combination of low-level features into high-level features, and can improve the efficiency of the model's calculations for investment prediction of power grid enterprises.
With the concept of sustainable development and green economy becoming important themes of current development in various fields, the focus on accelerating the transformation of economic development models has highlighted resource-saving and environment-friendly attributes of society. With the exponential growth of electricity consumption and the expansion of the scale of investment, the critical factors affecting investment in power grids have become increasingly diversified. Investment in power grid enterprises is no longer only affected by traditional factors but is also affected by emerging factors brought by the advancement of power grids. For example, the income model of power grid investment is greatly affected by power system reform [
For power grid investment forecasting, domestic and foreign scholars have put forward different ideas and solutions. At present, statistical prediction models and artificial intelligence prediction models are widely used in the field of power grid investment prediction. Traditional statistical prediction methods mainly include principal component analysis, regression algorithms, Kalman filter, and clustering. These methods have simple principles, but have longer periods, slower speeds, and larger errors. The intelligent algorithms proposed on the basis of traditional prediction methods include co-integration theory, particle swarm optimization theory, fuzzy analysis, back propagation neural network (BPNN), and support vector machines (SVM). These algorithms are intelligent and personalized, so they are widely used in power grid investment forecasting.
In terms of traditional statistical forecasting models, regression analysis is widely used. For example, Chen [
In addition to the above algorithm, some scholars have used other methods to analyze power grid investment. Liu et al. [
In the field of forecasting, RBM plays an important role. For example, Shi et al. [
However, the generalization ability of RBM is low, and its fixed training rate is not conducive to the network jumping out of the minimum point. Some experts have begun to use various algorithms to optimize it. Cho et al. [
Combined with the advantages and improvements of previous methods, this paper proposes a new power grid investment forecasting model based on the DRBM optimized by the Lion algorithm (LA). Considering the characteristics of power grid investment prediction, transmission and distribution price reform (TDPR) and 5G station construction are integrated into the influencing factors for comprehensive consideration. We analyzed the influence factors of power grid investment using a fuzzy threshold method and constructed a power grid investment prediction model using a deep restricted Boltzmann machine optimized by the Lion algorithm (LA-DRBM). The model improved the global search ability and enhanced the abstraction ability of high-dimensional complex data through layer-by-layer feature transformation. Then, a regression model, BPNN, SVM, RBM model, and LA-RBM model were selected for comparative analysis. The empirical study illustrates that the prediction accuracy and generalization performance of the model have been effectively improved.
The rest of the paper is arranged as follows:
RBM is a non-feedback random neural network model with two layers. One layer is the visual layer for inputting observation data, and the other layer is the hidden layer for feature extraction [
The RBM is a typical model based on energy function. For a given state
On the basis of the energy function of
Under the assumption that the hidden layer unit and the visible layer unit are binary variables, which means
As the RBM structure is symmetrical, the activation probability of the
The objective of optimizing parameters in the RBM is to maximize the likelihood function
Considering the complexity of likelihood function calculation, this paper uses the reconstruction error instead of the likelihood function for the evaluation function of the RBM. The reconstruction error takes the training sample as the initial state and calculates the difference with the original data after several block Gibbs samples. In the training process of the RBM, the reconstruction error of the
When the error condition is met, the output result of the hidden layer is the output result of the system.
Considering that the RBM, as a single-layer structure, has insufficient ability to extract and process information, this paper takes the RBM as the basic network structure, constructs a multi-layer network structure, and forms a DRBM model [
Compared with the RBM, the DRBM can extract more abstract space vectors from high-dimensional complex input data through layer-by-layer feature transformation. In addition, the DRBM can train a large number of unlabeled sample data to reduce the impact of error or redundant information on the output results so as to increase the accuracy of the prediction results.
The training set is substituted into the DRBM model for training, and the network output result is the prediction result.
At the beginning of the 21st century, Rajakumar proposed the Lion algorithm [
The algorithm can be divided into four parts according to the behavior characteristics of the lion group: initial population, mating and mutation, territorial defense, and territorial takeover. Firstly, a random individual is selected as the starting point, and each lion is taken as a solution vector to search according to the target. Then, in these feasible solutions, an iterative operation is carried out. When the termination condition is reached, the optimal solution is obtained. Therefore, the fitness function of the model needs to be determined. Taking this as the basis of the search iteration, suppose the fitness function is:
Initialize the pride
We first set the population size and set the number of lions as The pride
Mating is an effective way to generate new individuals. Therefore, mating in the algorithm can make existing solution vectors generate new feasible solution vectors. The process includes steps such as crossover, mutation, clustering, and elimination of weak individuals.
In the algorithm, the crossover based on double probability is introduced; that is, two different probabilities are used for crossover to produce offspring. After
In the algorithm, mutation is used to generate a new cub by a random mutation with probability
In order to group and cluster, the k-means method can be used for gender grouping of the existing eight species of offspring, which are divided into male cubs (
Finally, in order to update the cub group and maintain its stability, the health status of the two groups of the cubs was compared, and the weak individuals of the excessive group were eliminated so that the number of the two groups of cubs was maintained in balance. Additionally, after the population is updated, the age of the cub is initialized to 0.
Territorial defense
Territorial defense is a unique behavior of lions. In order to protect the group and demonstrate loyalty, male lions defend against attacks of nomad lions. The defense process is shown in
Firstly, initialize the nomad lions
If Territorial takeover
The cubs begin to take over the territory when they ware mature, and they are compared as lions with the original lions. The optimal solution (
Let
Let
The DRBM is a multi-layer structure composed of the traditional RBM. The DRBM has similar problems as the RBM, in that it will become extremely slow due to pathological problems. Therefore, we applied the Lion algorithm to optimize the parameters of the DRBM.
The steps of optimizing the DRBM model by the Lion algorithm are as follows:
Initialize the DRBM model parameters. For the DRBM, the model parameter is Use the training vectors to train the DRBM model and calculate the evaluation function of the model. Randomly set the initial population of the Lion algorithm, take the objective function of the DRBM model as the fitness function of the Lion algorithm, and use the Lion algorithm to continuously optimize the model parameters.
The optimization of the DRBM model through the Lion algorithm can speed up the training efficiency of the model and improve the learning ability of the model. The analysis process of the LA-DRBM model is shown in
As an effective evaluation method of influencing factors, the fuzzy threshold method can judge the critical influencing factors by comparing the calculated fuzzy recognition value with the actual set threshold [ Determine the evaluation level. Each influencing factor must be evaluated according to certain standards. The evaluation levels are as follows:
Establish a fuzzy relationship matrix. Assuming that there are N experts in total, all the experts evaluate each influencing factor according to the grad, and the
where
Factors | Comment | ||||
---|---|---|---|---|---|
… | |||||
… |
Therefore, the fuzzy relationship matrix Determine the evaluation grade weight vector, and the vector is as follows:
where
It is determined that the weight settings have a great impact on the comprehensive evaluation results.
Calculate the evaluation result. Multiply the evaluation grade weight vector Based on experience, set thresholds and screen critical influencing factors.
There are many factors influencing power grid investment, which are closely related to the income of power grid companies, investment benefits, and customer needs [
The fuzzy threshold method is used to analyze the critical influencing factors of power grid investment, and the evaluation level
V = {Extremely important, very important, generally important, not very important, not important} is set, and the corresponding weight vector is determined as W = {0.3, 0.25, 0.2, 0.15, 0.1}. Then, the fuzzy relationship matrix is obtained by the expert scoring and calculating the overall membership degree and multiplying it with the weight vector W to obtain the fuzzy comprehensive evaluation result, as shown in
s1 | 7.55 | s6 | 6.23 | s11 | 1.20 | s16 | 6.21 |
s2 | 5.86 | s7 | 4.54 | s12 | 4.21 | s17 | 5.78 |
s3 | 2.4 | s8 | 5.61 | s13 | 5.62 | ||
s4 | 3.63 | s9 | 3.25 | s14 | 4.36 | ||
s5 | 4.95 | s10 | 6.85 | s15 | 3.11 |
According to the results of the comprehensive calculation and the actual situation, the threshold value is set to 5, and the influencing factors with a fuzzy comprehensive evaluation result greater than 5 are the critical influencing factors of power grid investment. It is concluded that the critical influencing factors include total assets s1, total electricity consumption s2, ratio of grid assets to income s6, increased load per unit of grid investment s8, power supply reliability s10, standard coal saving s13, influence factors of TDPR s16, and 5G station construction s17, as shown in
In the analysis of the influence factors of TDPR and 5G station construction, the two influencing factors are taken into account by introducing correction factors. The correction coefficient of TDPR is
According to the analysis of the fuzzy threshold method, the critical influencing factors of power grid investment are total assets s1, total electricity consumption s2, ratio of grid assets to income s6, increased load per unit of grid investment s8, power supply reliability s10, standard coal saving s13, influence factors of TDPR s16, and 5G station construction s17. We took a provincial power grid company (Z power grid company) in the southeast region of China as the research object, and selected data from 2010 to 2020 as samples for analysis. The input vector of the prediction model was {s1, s2, s6, s8, s10, s13}, and the output vector was the investment amount
In order to reduce the influence of dimensions and units on the prediction results, the extreme value method is used to normalize the original data. The calculation formula is as follows:
We can use the data of Z grid company from 2010 to 2019 as training set to train the model and use the trained model to predict the company's investment in 2020.
First, set the Lion algorithm parameters, as shown in
Parameter | Value | Parameter | Value |
---|---|---|---|
N | 6 | 150 | |
3 | Crossover probability | [0.45, 0.55] | |
5 | Mutation probability |
0.35 |
Then, the lion algorithm is used to iteratively optimize the parameters of the DRBM. At the same time, based on experience, set the correction coefficient
Model | Train time (s) | Forecast time (s) |
---|---|---|
RBM | 375.92 | 0.120 |
LA-RBM | 322.64 | 0.116 |
According to the graphic analysis, the forecasting value of the sample largely coincides with the actual value, and the fitting effect is excellent.
Calculate the relative error rate (RER) and non-linear function goodness of fit (R2) between the actual value and the forecasting value of the training sample. The calculation formula of the RER and R2 is:
The RER of the training result is shown in
The analysis result shows that the RERs of the training results are between −0.02 and 0.015, which are small, indicating that the training effect of the model is excellent. Therefore, it can be considered that the model is an effective method to predict the investment amount of power grids.
Finally, we can use the optimized model to forecast the investment of Z grid companies in 2020.
The prediction effect of this model is compared with the prediction effect of the BPNN, SVM, RBM, and regression model. The comparison results are shown in
Investment amount (million CNY) | RER (%) | R2 | ||
---|---|---|---|---|
Actual value | 658.46 | – | ||
Prediction value | LA-DRBM | 646.95 | −1.75 | 0.9825 |
RBM | 676.58 | 2.75 | 0.9725 | |
SVM | 633.11 | −3.85 | 0.9615 | |
BP | 621.57 | −5.60 | 0.9440 | |
Regression model | 679.77 | 3.24 | 0.9676 |
According to the analysis results, the RERs for Z grid enterprise's investment forecast in 2020 of the LA-DRBM model, the RBM model, the SVM model, the BP model, and the regression model are −1.75%, 2.75%, −3.85%, −5.60%, and 3.24%, respectively. Among them, the RER of the LA-DRBM model is the smallest. From the analysis of the results, the LA-DRBM model can achieve a good prediction effect and high prediction accuracy in forecasting power grid investment.
In order to respond to the requirement of sustainable development of power grid investment and improve the accuracy of power grid investment prediction, a new power grid investment prediction model based on the LA-RBM (deep restricted Boltzmann machine optimized by the Lion algorithm) model is proposed according to the characteristics of power grid planning and the trend of power grid investment. In this paper, the main findings and conclusions are as follows:
Firstly, the fuzzy threshold method was used to analyze the influencing factors of a power grid enterprise's investment. After analysis, 8 critical influencing factors were screened out of 17 influencing factors, including total assets s1, total electricity consumption s2, ratio of grid assets to include s6, increased load per unit of grid investment s8, power supply reliability s10, standard cost saving s13, influence factors of TDPR s16, and 5G station construction s17. These factors were used as input variables to train the model. Then, considering the problem of the insufficient solution speed of the DRBM model, the Lion algorithm was used to optimize the parameters of the DRBM model. The optimized parameter set of the DRBM model was Finally, the LA-RBM power grid investment forecast model was compared with the RBM, SVM, and BPNN. According to the analysis results, the RERs for Z grid enterprise's investment forecast in 2020 of the LA-DRBM model, the RBM model, the SVM model, and the BP model were 3.22%, 7.92%, 9.01%, and 10.77%, respectively. Among them, the RER of the LA-DRBM model was the smallest. From the analysis of the results, the LA-DRBM model can achieve a good prediction effect and high prediction accuracy in forecasting power grid investment.
Therefore, the experimental results fully prove that the LA-DRBM power grid investment forecast model has strong generalization ability and robustness; the prediction accuracy is better than other models; and it can achieve good prediction results. This model provides new ideas and references for power grid investment forecasting.
The innovations of the paper are as follows:
The LA-DRBM model can abstract the combination of low-level features into high-level features so as to adequately reflect the data characteristics and improve the computational efficiency of the model. The LA-DRBM model uses the Lion algorithm to optimize the deep restricted Boltzmann machine, which improves the forecasting accuracy of the model. The article introduces the TDPR and 5G station construction as influencing factors, which were used in an innovative index system of influencing factors. The model can combine the advantages of a single model and overcome the shortcomings of a single model. The combined model is thus an effective prediction method for power grid investment and has a strong generalization ability and good robustness.