# Application of Improved Deep Learning Method in Intelligent Power System

Authors: HuiJie Liu; Yang Liu; ChengWen Xu
Journal: International Transactions on Electrical Energy Systems
 (2022)

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## Abstract

In view of the inaccurate short-term power load prediction in the power system, where the smart grid cannot effectively coordinate the production, transportation, and distribution of electric energy, the authors propose the application of improved deep learning methods in intelligent power systems. The method uses the convolutional neural network to establish the energy prediction calculation model, uses CNN adaptive data features to mine characteristics, quantifies power uncertainty, uses drop regularization to optimize the deep network structure, uses the deep forest to learn the extracted data features, and builds a prediction model, in order to achieve accurate prediction of power load and solve the problem that the accuracy of existing forecasting methods decreases due to random fluctuations of power. The results showed the following: in the power load forecast results over the weekend, the random forest and the LSTM algorithm forecast results were relatively close and the RMSEs were 17.3 and 17.1, respectively, while the SVM predicted a larger RMSE error of 27.5. The authors’ method predicts the best with 14.8.Conclusion. After verification based on actual load data, in the case of uncertain fluctuations in power load, this method can accurately predict the power load, and the accuracy is higher than that of the more popular methods at present, and it is expected to become an important technical support for solving the core problems of smart grid.


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## Body

## 1. Introduction

With the rapid development of smart grids, this raises many concerns about the efficient use of the environment, sustainability, and energy independence; therefore, the construction of power load forecasting system has become the main purpose of power supply management. The development of smart grids has benefited from advances in information and communication technologies, which are increasingly becoming a powerful and efficient system. In this environment, research on more secure, reliable, efficient, and cost-effective smart grids has attracted extensive attention, as shown in Figure1. At present, the daily operation and planning of the smart grid require load forecasting for users one day in advance. The accuracy of intraday forecasting models is relevant to many decisions, including gas supply planning, security measures, financial planning for power generation, and e-business planning. However, predicting the next day is a difficult task as it depends on other factors like weather and probability. In order to achieve this, it is important to reduce the uncertainty related to the demand and to meet the requirements for the product. To achieve this goal, it is necessary to understand the characteristics of demand forecasting, and based on this, it is necessary to improve or select an optimal model for short-term load forecasting. The problem of short-term load forecasting can be considered as a time forecasting problem, that is, based on the current load forecasting load, a series of neural network models is integrated for forecasting, which further improves the accuracy of load forecasting. The neural network algorithm is only used for high-accuracy short-term energy load forecasting; the neural network algorithm only has a small number of hidden nodes, which will limit the investment properties of some casino problems in researching the network structure. This is very important in the study of machine learning algorithms. On the other hand, during short-term energy load forecasting, the peak load is considered to be an important factor affecting the stability of the smart grid. Despite the accuracy of the machine learning algorithm, both overestimation and trough can lead to power loss at peak loads. In some cases, the weekly maximum forecast is the short-term forecast goal because it is the most important in the short term. Power sector operations such as electricity production, safety measures, and energy conservation are based on the results of short-term peak load forecasting. Therefore, it is necessary to improve the accuracy of short-term energy load forecasting.Figure 1
Intelligent power system.


## 2. Literature Review

The origin of short-term power load forecasting is early, and many researchers have done a lot of research on power load forecasting, so there are many methods for power load forecasting, and they have achieved good results [1]. For short- and medium-term electricity, simple linear regression, multiple linear regression, nonlinear regression, the artificial neural network (ANN), and the support vector machine (SVM) among others have been usedby many researchers. Linear regression was used for load forecasting. Zhang et al. combined the ant colony algorithm with gray theory; the gray ant colony neural network is established, and the feedback algorithm is added on the basis of the GM (1,1) model, after which the unique advantages of the two can be exerted, and the expected results can be obtained [2]. Chen and Chen used autoregressive modeling to develop a method based on nonlinear load regression and modified it with chaos theory, successfully reducing the influence of local extreme values on the prediction results [3]. In their report, Ngoc chose to use the Grassberger–Procaccia algorithm for short-term load prediction by chaotic dynamic reconstruction, and the least-squares regression method was used to obtain residual values relevant factor [4]. Yu Investigated a random forest model for predicting short-term energy loads, which is a multiple regression tree (CART) research [5].Souza chose a neural fuzzy structure that can be defined as ANN(artificial neural network), which is composed of experimental data and can find the system parameters of fuzzy reasoning. Selection of a neural fuzzy model can be described as the ANN (Artificial Neural Network) with research data to find collision-free systems [6]. Lakhmiri ANN-based short-term load forecasting[6]. Lakhmiri ANN-based short-term load forecasting models are excellent, and the most common type of the ANN is the multilayer perceptron (MLP), which uses previous load data to estimate the load curve. As we know, network structure plays an important role in neural networks, because information about the structure (including estimated time or change) is reflected in the neural network structure [7]. Liu et al. proposed an ANN-based time prediction model for home use [8].On the basis of current research, the authors propose the application of improved deep learning methods in intelligent power systems. A new convolutional neural network-deep forest prediction method is used; first, the energy prediction calculation model is established by using the convolutional neural network, and the power uncertainty is quantified by combining it with the monte carlo algorithm. Second, the obtained uncertainty evaluation features and power distribution features are input into the deep forest to accurately predict short-term power loads [9]. The specific workflow of this method is shown in Figure 2:Figure 2
A new method for accurate prediction of power load.Sufficient short-term power load data time series is sorted out under similar working conditions from historical data, and the deep convolutional network is fed into as training samples. The deep convolutional network utilizes multilayer convolution and pooling; the authors propose the potential laws hidden in the data that are difficult to describe or discover by analytical methods and store them in the form of graph data [10]. The authors use dropout regularization to optimize the deep network structure and realize the uncertain quantification of extracted features through the uncertain evaluation of model parameters; therefore, it is possible to consider the influence of the uncertainty of the original data on the prediction results. Finally, the authors use the deep forest to learn the extracted data features and establish a prediction model to achieve an accurate prediction of power load.


## 3. Methods

### 3.1. Deep Convolutional Networks

The deep convolutional network is a popular deep learning algorithm, which can effectively identify the spatial relationship between elements of complex matrix and extract key data features according to the theoretical basis of the deep convolutional network. The CNN model developed by the authors includes an input layer, two layers (C1 and C3), two layers (P2 and P4), a fully connected layer (FC5), and an output layer (Softmax). A deep convolutional network uses a convolutional kernel to extract texture features from the input (image) matrix, and the convolution process descends the feature map, reduces the map dimension, and extracts local features. After two folds and merge layers, the original image features can be extracted. After that, the fully connected layer (FC5) is a high-level process to show the characteristics of different groups. The output method uses the Softmax function to give the result of the distribution [11]. To achieve this, a deep process is added between the second integration layer and all connected processes for later processing. In this way, the effectiveness of deep convolutional network training will become clearer, the extraction of sensitive feature information will be more accurate, and it will provide a basis for future predictions.


### 3.2. Monte Carlo—Discard Regularization

In order to measure the uncertainty of the parameters of the deep convolutional network model, the authors adopt a Monte Carlo dropout regularization (Monta Carlo-dropout regularization) algorithm, using the Monte Carlo uncertainty estimation capability to quantify the uncertainty of the model parameters; this indirectly reflects the hidden uncertainty of the power load data, making the final prediction result accurate and reliable [12].In general, a deep convolutional network can be represented by a functionfwx, where x is the network input and w is the network weight. The output of the network after training is y=fwx. The prediction for the new sample x∗ is y∗=fwx∗. In order to measure the uncertainty of the network, the calculation process of the Monte Carlo-drop regularization is as follows: first, use the new data x∗ to test the trained convolutional network and randomly discard the intermediate layer neurons N times with a fixed probability p while calculating the prediction to obtain a set of predicted value vectors y∗1y∗2⋯y∗N. From this, the uncertainty of the network prediction can be evaluated as follows:(1)Varfwx∗=1N∑i=1Nyi∗−y¯∗2,e=Varfwx∗.In the formula,Varfwx∗ is the variance of N times Monte Carlo forecast, y¯∗ is the average value of N times Monte Carlo forecast, and e is the forecast uncertainty. In this way, using Monte Carlo dropout, the prediction uncertainty can be evaluated, and the evaluation results can be sent to the subsequent prediction model for learning and memory; in this way, the new data will be adaptively compensated for the influence of uncertainty, and the prediction results will be accurate and stable.


### 3.3. Deep Forest

The deep forest (gcForest) is a method for deep exploration of random forests. In a random forest, a subset of data setsL is first generated by bootstrapping them using the first data set x. Then, each subdataset is used to create a decision tree, and all subdatasets form a forest containing L decision trees. Finally, each decision tree is generated, and the final outcome of the random forest is determined by a voting or averaging strategy. Deep forest implements the entire decision tree as a random forest, and class classification probabilities are generated by calculating the percentage of different classes in the report [13]. Therefore, the yield of a deep forest is defined as the result of the distribution of all trees identified in the forest. Deep learning techniques are used in deep forests using multivariate analysis (MGS) and cascade forests. The goal of MGS is to extract useful information from an input image as follows: first, each grayscale image (M × M matrix; M is the size of the image) is printed by a sliding window (window size k) to generate a subimage S. Each subgraph is a k × k matrix. If the slip is j, then S = [(M-k)/j + 1]2. Then, when training all random forests simultaneously, using each small image, the output vector of each forest has points C and corresponds to the result of the class letter C for information. The output vectors of the two training forest models are combined into 2C key feature vectors for each subimage. Therefore, for each grayscale image, both forest models generate a feature matrix of dimension S × 2C. Finally, collect each column of the feature matrix to obtain a 2 × S × C visual probability vector based on the MGS output of each gray image. A multisliding window can be used to scan grayscale images so that output vectors can be generated for each grayscale image. In this study, we use a sliding window with grayscale image size M = 28, sliding window size k = 26, and sliding window size j = 1, so the number of images is sub S = 9. The cascade forest is the baseline. The deep forest is used to implement deep learning strategies. It accepts a vector of MGS results and outputs the final distribution. A matte forest has a multilayer structure, each layer has two random forests and two random forests [14]. Similar to the MGS forest, each sampled random forest in the cascade forest produces a result vector of C elements, so the output length of each layer is 4C. The number of layers is determined during training, and learning is verified using cross-validation at each layer. For each grayscale image, the input to the first layer is the MGS probability P(=S × 2C). Then, the output of the first layer (i.e., 4C probability elements) is combined with the original P probability elements, and a new vector (i.e., 4C + P probability elements) is generated as input to the second layer. Similar connections are repeated to form the input vectors of the following layers up to the last layer. The results of the four forest models in the last layer are averaged to create the final result for class C. For the deep forest, the maximum of the final result is used [15]. Because the number of layers in the cascade forest is determined adaptively based on the training performance, the complexity of the model can be adjusted to multivariate data, which is more efficient than deep neural networks. Therefore, the authors’ energy load forecasting plan can flexibly process different sets of data, adapt to changes in the data set, and produce stable and reliable accurate predictions.


## 3.1. Deep Convolutional Networks

The deep convolutional network is a popular deep learning algorithm, which can effectively identify the spatial relationship between elements of complex matrix and extract key data features according to the theoretical basis of the deep convolutional network. The CNN model developed by the authors includes an input layer, two layers (C1 and C3), two layers (P2 and P4), a fully connected layer (FC5), and an output layer (Softmax). A deep convolutional network uses a convolutional kernel to extract texture features from the input (image) matrix, and the convolution process descends the feature map, reduces the map dimension, and extracts local features. After two folds and merge layers, the original image features can be extracted. After that, the fully connected layer (FC5) is a high-level process to show the characteristics of different groups. The output method uses the Softmax function to give the result of the distribution [11]. To achieve this, a deep process is added between the second integration layer and all connected processes for later processing. In this way, the effectiveness of deep convolutional network training will become clearer, the extraction of sensitive feature information will be more accurate, and it will provide a basis for future predictions.


## 3.2. Monte Carlo—Discard Regularization

In order to measure the uncertainty of the parameters of the deep convolutional network model, the authors adopt a Monte Carlo dropout regularization (Monta Carlo-dropout regularization) algorithm, using the Monte Carlo uncertainty estimation capability to quantify the uncertainty of the model parameters; this indirectly reflects the hidden uncertainty of the power load data, making the final prediction result accurate and reliable [12].In general, a deep convolutional network can be represented by a functionfwx, where x is the network input and w is the network weight. The output of the network after training is y=fwx. The prediction for the new sample x∗ is y∗=fwx∗. In order to measure the uncertainty of the network, the calculation process of the Monte Carlo-drop regularization is as follows: first, use the new data x∗ to test the trained convolutional network and randomly discard the intermediate layer neurons N times with a fixed probability p while calculating the prediction to obtain a set of predicted value vectors y∗1y∗2⋯y∗N. From this, the uncertainty of the network prediction can be evaluated as follows:(1)Varfwx∗=1N∑i=1Nyi∗−y¯∗2,e=Varfwx∗.In the formula,Varfwx∗ is the variance of N times Monte Carlo forecast, y¯∗ is the average value of N times Monte Carlo forecast, and e is the forecast uncertainty. In this way, using Monte Carlo dropout, the prediction uncertainty can be evaluated, and the evaluation results can be sent to the subsequent prediction model for learning and memory; in this way, the new data will be adaptively compensated for the influence of uncertainty, and the prediction results will be accurate and stable.


## 3.3. Deep Forest

The deep forest (gcForest) is a method for deep exploration of random forests. In a random forest, a subset of data setsL is first generated by bootstrapping them using the first data set x. Then, each subdataset is used to create a decision tree, and all subdatasets form a forest containing L decision trees. Finally, each decision tree is generated, and the final outcome of the random forest is determined by a voting or averaging strategy. Deep forest implements the entire decision tree as a random forest, and class classification probabilities are generated by calculating the percentage of different classes in the report [13]. Therefore, the yield of a deep forest is defined as the result of the distribution of all trees identified in the forest. Deep learning techniques are used in deep forests using multivariate analysis (MGS) and cascade forests. The goal of MGS is to extract useful information from an input image as follows: first, each grayscale image (M × M matrix; M is the size of the image) is printed by a sliding window (window size k) to generate a subimage S. Each subgraph is a k × k matrix. If the slip is j, then S = [(M-k)/j + 1]2. Then, when training all random forests simultaneously, using each small image, the output vector of each forest has points C and corresponds to the result of the class letter C for information. The output vectors of the two training forest models are combined into 2C key feature vectors for each subimage. Therefore, for each grayscale image, both forest models generate a feature matrix of dimension S × 2C. Finally, collect each column of the feature matrix to obtain a 2 × S × C visual probability vector based on the MGS output of each gray image. A multisliding window can be used to scan grayscale images so that output vectors can be generated for each grayscale image. In this study, we use a sliding window with grayscale image size M = 28, sliding window size k = 26, and sliding window size j = 1, so the number of images is sub S = 9. The cascade forest is the baseline. The deep forest is used to implement deep learning strategies. It accepts a vector of MGS results and outputs the final distribution. A matte forest has a multilayer structure, each layer has two random forests and two random forests [14]. Similar to the MGS forest, each sampled random forest in the cascade forest produces a result vector of C elements, so the output length of each layer is 4C. The number of layers is determined during training, and learning is verified using cross-validation at each layer. For each grayscale image, the input to the first layer is the MGS probability P(=S × 2C). Then, the output of the first layer (i.e., 4C probability elements) is combined with the original P probability elements, and a new vector (i.e., 4C + P probability elements) is generated as input to the second layer. Similar connections are repeated to form the input vectors of the following layers up to the last layer. The results of the four forest models in the last layer are averaged to create the final result for class C. For the deep forest, the maximum of the final result is used [15]. Because the number of layers in the cascade forest is determined adaptively based on the training performance, the complexity of the model can be adjusted to multivariate data, which is more efficient than deep neural networks. Therefore, the authors’ energy load forecasting plan can flexibly process different sets of data, adapt to changes in the data set, and produce stable and reliable accurate predictions.


## 4. Results and Discussion

One key smart grid technology is efficient energy management and demand-side implementation. Among them, short-term energy load forecasting is of particular importance. In order to solve this problem, the authors propose to use deep learning techniques in the power of intelligence and a new method of deep learning taking into account the uncertainty of prediction. To analyze the effectiveness of the method, the historical data of the energy load of the power plant over a period of time was analyzed [16]. The power grid continuously records the complete power load data in 2021; due to the large amount of data, Figure 3 only shows the complete power load historical data for two days. It can be seen from the data curve that the original power load data show obvious fluctuations, indicating that there are many uncertainties in the power load during the operation of the power grid, so that the data curve does not show useful laws or trends.Figure 3
Two-day power load historical data.Furthermore, the authors conducted a detailed analysis of the historical data in order to grasp the characteristics of the data in advance and analyze the frequency of power load fluctuations, especially the influence of different time periods and seasons on power load fluctuations, so as to provide ideas for the analysis of historical data. Through careful analysis of the historical data, it is found that the distribution of the power load curve on weekends (Saturday and Sunday) at each time point is relatively similar; on weekdays (Monday to Friday), due to the complex and changeable power demand, the distribution of the power load curve at different time points is more random, and there is no regular probability distribution; therefore, it is more difficult to predict on weekends than on weekdays. In addition, through the analysis, it is also found that the curve fluctuations of the data are more frequent in summer than in other seasons, showing more complex uncertainties, thus bringing more adverse effects on power load forecasting. In order to quantitatively analyze the fluctuation of the power curve of the power grid in 2021 on weekdays, weekends, and summer, the variance calculation of the data in these three time periods is carried out, and the results are shown in Table1 [17].Table 1
Power load fluctuation variance.
PeriodVolatility variance/(MVh)Weekend2719.1Working day3554.3Summer4652.7From the calculation results in Table1, it can be seen that the power fluctuation degree of the historical data is for weekends, weekdays, summer, and other time periods. It is foreseeable that due to the uncertainty brought about by fluctuations, the difficulty of forecasting is in the weekends, working days, summer, and other time periods. However, if the uncertainty of power load fluctuations can be well controlled, the prediction accuracy in different time periods can be reduced, and stable and accurate power load prediction results can be output. In order to verify this academic point of view, the historical data were predicted for different time periods. In order to test the prediction accuracy, two metrics, the root mean square error (RMSE), and the mean absolute percentage error (MAPE) [18] were used. The results are shown in Tables 2–4. Among them, the more popular methods such as LSTM, SVM, and random forest are compared. Table 2 shows the predicted results of power load on working days. In the analysis, the complete power load data of the grid for 100 consecutive working days (excluding weekends) were selected [19]. From the prediction results, the prediction results of random forest and the LSTM algorithm are relatively close, more accurate than the SVM but not as good as the authors’ method. This is because the author’s method analyzes the uncertainty of the model, and the network will adaptively compensate for the effects of random power fluctuations.Table 2
Workday power load forecast results.
Method of predictionPrediction accuracyRMSEMAPESVM26.10.025Random forest19.50.023LSTM19.30.023Method17.20.021Table 3
Weekend power load forecast results.
Method of predictionPrediction accuracyRMSEMAPESVM27.50.026Random forest17.30.022LSTM17.10.022Method14.80.020Table 4
Three-month power load forecast results in summer.
Method of predictionPrediction accuracyRMSEMAPESVM35.10.033Random forest27.80.027LSTM27.50.026Method18.30.022Table3 shows the forecast results of the power load for the weekend. The complete power load data of the grid for 100 consecutive weekends (excluding weekdays) were selected for the analysis. From the predicted results, the prediction results of random forest and LSTM algorithms are relatively close, with RMSE of 17.3 and 17.1, respectively, while the SVM prediction has a larger RMSE error of 27.5; The authors’ method predicts the best with 14.8. This is because the authors’ method can utilize the adaptive ability of the deep forest to sample size, so this method adjusts the forest parameters according to the actual sample size, in order to achieve accurate predictions for different samples [20].Table4 specifically analyzes the power load forecast results for the three months in summer. Due to the large fluctuations in electricity consumption in summer, the resulting uncertainty also increases. From the prediction results, the RMSE predicted by the random forest and LSTM algorithms are 27.8 and 27.5, respectively, and the SVM prediction RMSE error is 35.1. The prediction effect RMSE of the authors’ method is 18.3. It can be seen that the power fluctuation has a great influence on the prediction accuracy, but the authors’ method can still accurately predict the power load. It can be seen that the authors’ method is a reasonable and effective power load forecasting method.


## 5. Conclusion

Aiming at the key problem of accurate prediction of power load in the current smart grid system, the authors propose the application of the improved deep learning method in the intelligent power system, which can solve the problem that the accuracy of existing prediction methods is reduced due to random fluctuation of power. The actual power data analysis results show that due to the uncertainty evaluation based on discarding regularization, the proposed deep learning method can accurately predict the power load in the case of large power fluctuations, and the accuracy is higher than that of the more popular methods. Therefore, given the stability and effectiveness of the authors’ method, it is expected to provide important technical support for solving the core problems of smart grids.



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