Customer Load Forecasting Method Based on the Industry Electricity Consumption Behavior Portrait

Abstract

With the dramatic increase of energy demand and the continuous increase of power system operation pressure, higher requirements are put forward for the development of power grid planning and optimization operation. It is important for the refinement of distribution network planning to deeply extract the characteristics of user load. First, the process of load characteristic analysis method from the user level to the industry level is proposed, which achieves the division of electricity consumption patterns of various industries, thus building a panoramic portrait of industry electricity consumption behavior. Then, by expanding the information filled in by traditional customers, the feature vector of each user is extracted, and the users' industry electricity consumption patterns are used as the label. Therefore, a method for identifying the electricity consumption pattern of the customer based on the BB-stacking model fusion framework is proposed, which yields the preliminary forecast results of customer load based on the actual load accounting results of the customers. Finally, comparative simulations with different methods verify the effectiveness of the proposed algorithm, which can provide prominent guidance for the actual distribution network planning work.

Introduction

Development of Customer Portrait

More recently, with the gradual transformation of enterprises from product oriented to user oriented in the production, it is significant for formulating marketing strategies and product design to fully understand customers and their needs (Wu et al., 2020). At present, in-depth mining of user data and research on accurate user portrait are gradually deepening. Accurate user portrait technology has been widely used in the Internet, finance, retail, operators, advertising, and other industries (Liu and Du, 2020; Zhang et al., 2020). In terms of advertising, Google uses big data to locate high-value users, which helps mobile e-commerce app to locate high-value users and push advertisements and adopts flexible data tracking methods for users of different tag categories, so that brands can achieve accurate advertising push for target users (Shan, 2018). In terms of auxiliary business decision-making, eBay carries out business circle customer group analysis, product marketability analysis, store operation analysis, personalized consumption analysis, and customer loss analysis by integrating online and offline massive data, providing decision support for commercial real estate and comprehensive big data analysis and prediction for project parties and brands (Chen et al., 2021). Obviously, user portrait technology has become an effective method to improve customer service quality and customer experience, which is an important basis for integrating high-quality resources and realizing enterprise and user value (Pitner et al., 2012; Yu et al., 2017).

In the field of electric power research, it has become a rising research direction to build user portraits in different application scenarios according to the actual needs (Qiu et al., 2017). In the early stage of the study, traditional user portraits were mainly used in the marketing portal to build user electricity sensitivity or credit portraits to guide the electricity recovery work (Sanchez et al., 2008; Han et al., 2014; Ampimah et al., 2017). To meet the requirements of demand response, the methods of establishing electricity consumption behavior tag library and realizing the portrait of different types of users' electricity consumption behavior patterns were proposed in (Qiu et al., 2017) and (Zhong et al., 2018). With the gradual popularization of new energy (Yang et al., 2016; Yang et al., 2017; Yang et al., 2018; Yang et al., 2019a; Yang et al., 2019b; Yang et al., 2020), the load change and influence mechanism on the user side are becoming more and more complex. Therefore, the user portrait method is becoming more and more important.

The above pieces of literature are all portraits of the users who have been connected to the power grid, so as to formulate appropriate strategies to guide the corresponding users to change their electricity consumption behavior. However, there is rare research on the reported customers who are not connected to the power grid. In the distribution network planning work, reference (Lian et al., 2014) makes use of the complementarity between user loads to optimize the access decision reasonably, which can effectively improve the load distribution and utilization rate of power supply equipment. Moreover, the multidimensional analysis of user load can also provide effective guidance for the optimal scheduling and control of smart grid (Yang et al., 2015; Zhang et al., 2015; Xi et al., 2016; Zhang et al., 2016; Zhang et al., 2021). However, due to the lack of research on the load of customers who are not connected, it is limited to transfer the user load of the existing distribution network. For the more common scenario of business expansion, the data information support and optimization ability are insufficient.

Research and Contribution of the Paper

Portrait of Industry Electricity Consumption Behavior

In this paper, a load characteristic analysis method from user level to industry level is proposed. First, the massive users of various industries in the region are systematically analyzed and integrated. Therefore, a comprehensive and practical industry electricity consumption behavior portrait in the region is profiled. The key steps are shown in Figure 1.

FIGURE 1

Load Data Preprocessing

For continuous load data, Lagrange interpolation (Criscuolo et al., 1984) is used to fill the vacancy and bad data, which can play a good repair effect. For the data points that need to be filled, normal data points adjacent to the point are selected as samples to calculate the Lagrange interpolation formula:where and are the sampling time of the and data points in a day, means the load value of the data point, and is the number of sample points used to construct the polynomial. Finally, the approximate value of the missing value can be obtained by substituting the missing time into .

To avoid the influence of the actual load value in the follow-up analysis process, the linear proportion normalization method is used to normalize the load data after repair, which can be expressed aswhere represents the -th load data point after normalization and is the peak load of the user.

Load Analysis of User Level

User Load Curve Analysis

User Load Characteristics Analysis

The user load curve can directly describe the change trend of user load in a day, and the load characteristics can more comprehensively characterize the user's electricity behavior from multiple perspectives. In this paper, user load characteristics are selected and calculated from three aspects, i.e., user load level, typical peak-valley characteristics, and load development law. The names and expression of each index are shown in Table 1.

In Table 1, the load level indicators represent the volume of user power consumption, which is the key information in the distribution network planning. Indicators of typical peak-valley characteristics can reflect the changing trend of user load in a day with less information granularity. The index of long-term load development reflects the rule that a user's load gradually increases to saturation after access to electricity and the relationship between saturated load and its area occupied. It is an important basis for reasonable estimation of the user's load.

Preliminary Classification of Users Based on Industry Classification Standards

To serve distribution network planning, the results of single user load characteristic analysis lack universality. Thus, it is necessary to explore the general law of massive users’ load changes.

Generally, the users are classified into various industries according to the National Economic Industry Classification national standard, forming a user group in each industry, which has similarity in the long-term development law of load. In this way, the summary of the law and subsequent application is facilitated. After the preliminary classification of users is completed, due to the different characteristics of user peaks and valleys, it is necessary to further carry out industry power mode analysis and feature extraction.

Load Analysis of Industry Level

Analysis of Industry Electricity Consumption Pattern

Characteristic Extraction of Industry Electricity Consumption

After completing the division of industry electricity consumption pattern, it is necessary to extract the corresponding characteristics of each electricity consumption pattern in the industry. Considering the nature of the characteristics, it is not significant to summarize and extract the load level and typical peak-valley characteristics at the industry level. Considering the actual needs of the power grid, it is of great significance to extract the long-term load development law of various electricity consumption patterns in the industry.

Nonparametric kernel density estimation (Lambert et al., 1999) is a data sample-driven method, which can fit the probability distribution of features without prior knowledge. In this paper, a nonparametric kernel density estimation method is used to fit the probability density of long-term load development characteristics, and then typical characteristics are extracted.

Taking the saturated load density as an example, suppose that there are users belonging to industry electricity consumption pattern , where the saturated load density of user is ; then the probability density function of can be computed bywhere means the kernel function; denotes the bandwidth.

To ensure the continuity of the probability density function, the kernel function needs to be a smooth probability density function. Generally, Gaussian kernel function is often selected, which can be expressed as follows:

The point with the largest value of the probability density fitting function is selected as the typical characteristic value of the electricity consumption pattern , which can be computed bywhere stands for the typical saturated load density value of the electricity consumption pattern .

Construction of Industry Electricity Consumption Behavior Portrait

After the load analysis from the user level to the industry level, the load curves and related characteristics of the power users and industry modes are obtained, respectively, at the user level and the industry level, which is the description of the electricity consumption behavior of the industry from different angles and at different levels.

To enable the distribution network planners to grasp the electricity consumption of various industries intuitively, massive analysis results are sorted and visualized. Then, a panoramic portrait of industry electricity consumption behavior in a region is built. The form and content of the portrait are shown in Figure 2.

FIGURE 2

Pattern Classification of New Customers' Electricity Consumption

In the traditional business expansion and installation, for the customers who have not been connected to the power grid, the planners are supposed to calculate the customer's load based on the customer's reported capacity and business experience, working out the business expansion and access scheme. The processing flow is extensive and lacks consideration for user load time sequence characteristics.

After the construction of a panoramic portrait of industry electricity consumption behavior in the region, if we can reasonably infer the electricity consumption pattern according to the relevant power consumption information provided by customers, it can provide more effective technical support for business decision-making of planners.

The basic flow of the electricity consumption pattern classification in this paper is shown in Figure 3. The training model obtains the electricity pattern classification ability by using the users’ characteristics as the training samples. After training the model, the corresponding characteristic vectors are extracted from the electricity consumption information of the customers and input into the classification model to obtain the electricity consumption pattern classification results of the customers.

FIGURE 3

Customer Classification Algorithm Based on BB-Stacking Model Fusion Framework

In the field of the classification problem, ensemble learning (EL) has gained the attention of a huge number of scholars, because it can make up for each single model's advantages (Wang et al., 2015). Among them, bagging (Hu et al., 2011) and boosting (Lu et al., 2006) are the most classic and widely used integration methods, which have their own characteristics in model generalization and model accuracy. Specifically, bagging builds multiple datasets in bootstrap way and trains multiple single models in parallel, integrating the output by Voting. The advantages of each model are integrated, preventing the overfitting phenomenon effectively. Boosting framework belongs to the mode of serial integration of models. The output of each model is used as the input of the lower model, and the minimum deviation is used as the loss function to continuously improve the accuracy of each model. Generally, the characteristics of the two integration methods are shown in Table 2.

To give full play to the advantages of the two integration methods, a model fusion framework of BB-stacking (bagging and boosting in stacking) based on the stacking method is proposed. It combines the two integration methods reasonably to realize the organic trade-off between high-precision and strong generalization performance, yielding the high-precision classification of customers.

Traditional stacking is composed of the base model in the lower layer and the metamodel in the upper layer. Firstly, the initial data is divided into subdatasets by k-fold, and then the subdatasets are input into the base model for training and classification. Then the output of the lower layer is reconstructed and sent to the upper model for training and classification. The BB-stacking model fusion algorithm proposed in this paper changes the lower base model layer into the integration layer on the basis of the two-tier structure of stacking, in which bagging and boosting methods are used to preliminarily integrate the original model and then transferred to the upper metamodel to coordinate the advantages of the two integration modes, obtaining a model with higher accuracy and more stable generalization ability.

Expansion of New Customers' Electricity Consumption Information

In the traditional business process, customers provide limited information in the information filling process. To make the pattern classification of electricity consumption more accurate, this paper proposes an appropriate expansion of the electricity consumption information reported by the customers. In addition to the traditional information of electricity consumption type, electricity consumption location, and electricity consumption capacity, the peak electricity consumption time schedule, peak electricity consumption level estimation, and valley electricity consumption level estimation are added as the customer's electricity consumption information. Table 3 shows the demonstration of extended power consumption information of a customer.

According to the example of extended electricity consumption information of the reported customer in Table 3, the reference load curve of the reported customer is obtained by time simulation. According to the definition of the peak-valley characteristic index in Table 1, the peak-valley characteristic vector of the customer can be calculated as the input of the electricity consumption pattern classification model.

Customer Load Forecasting

Calculation of Customer Load Level

Generally, the load level of industrial customers often depends on the capacity of production equipment and has weak correlation with the building area. It is recommended to use the utility rate method to calculate the customer load. Similarly, assuming that the classification result of the customer is industry electricity consumption pattern , the customer accounting load can be computed bywhere stands for the typical utility rate value of the electricity consumption pattern ; means the building area of the customer.

Forecasting of Customer Load Curve

After the customer load level estimation is completed, the load curve can be forecasted according to the electricity consumption pattern classification results of the customer in the industry. The forecasting load curve of the customer belonging to electricity consumption pattern is , and the calculation formula is as follows:where is the typical load curve of industry electricity consumption pattern .

Evaluation Index of Customer Load Forecasting Results

Case Studies

The dataset used in this paper is the load data of distribution transformer in an economically developed city from 2015 to 2019. One measurement point data is obtained every 15 min, with a total of 96 load data points per day. From the huge dataset, the distribution transformer data with high data quality and load development to saturation are selected. Combined with the corresponding industry information identification of distribution transformer users, the data cleaning and load characteristic analysis from user level to industry level are completed. Finally, the regional industry electricity consumption behavior image of the city is constructed.

In all industries, the metal products industry accounts for the highest proportion of users, which is a typical industry in the region. Taking the metal products industry as an example, this paper analyzes and demonstrates the output results and verifies the effectiveness of the proposed method.

Display of Industry Electricity Consumption Behavior Portrait

Analysis Results of User Level Load Characteristics

FIGURE 4

Similarly, take the user as an example, collect the user's relevant loading information, and calculate the corresponding user load characteristics, as shown in Table 4. It can be found that the user has large-scale production and the daily peak-valley difference is high. From the long-term development point of view, when the user load develops to saturation, the utility rate is low.

Analysis Results of Industry Level Load Characteristics

After extracting the typical load curve of 2,478 users in the metal products industry, the load curve of this user group is clustered. The minimum cluster number is set as two and the maximum cluster number is set as 8. Finally, the algorithm flow is executed, the average value of SC of each clustering result is calculated through Eqs 5–8, and the result is shown in Figure 5.

FIGURE 5

FIGURE 6

After dividing the typical electricity consumption pattern of the industry, the characteristic extraction of the utility rate of 342 users in electricity consumption pattern one is taken as an example. The bandwidth is set as 0.01. The probability density function of the user utility rate of the group is obtained by fitting Eq. 9, and the results are shown in Figure 7.

FIGURE 7

Customers’ Electricity Consumption Pattern Classification

To verify the effectiveness of load forecasting technology for new customers, 141 metal products’ users with complete electricity consumption data are selected as hypothetical customers. Therefore, the corresponding characteristic vectors are extracted and input into the trained BB-stacking model for electricity consumption pattern classification. In this paper, the traditional decision tree model (Safavian and Landgrebe, 1991), GRNN model (Specht, 1991), and PNN model (Oh and Pedrycz, 2002) are used to compare with the BB-stacking model. The classification accuracy under different training sample sizes is shown in Figure 8.

FIGURE 8

It can be found that, in the training process, the BB-stacking classification model can achieve a better classification effect than other classification models in a small training sample size. When the training sample size increases, the BB-stacking classification model can maintain a more stable generalization ability. It is obvious that the BB-stacking classification model prevents the phenomenon of classification ability decline caused by underfitting, performing better classification effect and stability.

Analysis of Customer Load Forecast Results

FIGURE 9

To further study the effectiveness of the proposed customer load forecasting method, this paper compares the traditional load estimation method with the proposed method. In addition, the sectional load curve obtained by the segment simulation method in the process of electricity consumption pattern classification is compared with the proposed method in this paper.

The comparison between the load curve forecasting result of a certain customer and other methods is shown in Figure 10. It can be found that there is little difference between the maximum load level of the estimated load and the actual load curve, but there is no time sequence characteristic. To some extent, the segment simulation method reflects the time sequence characteristics of customers, but the difference is large when the load level changes. The overall trend of the load curve forecasting result is very close to the actual load curve, which can meet the optimal access requirements in the planning field considering the time sequence characteristics of customers’ load.

FIGURE 10

Comparing the error index of actual load curve and load curve forecasting results under different methods, the mean value comparison of test samples is shown in Table 5. It can be found that the proposed method performs best in both of the two evaluation indexes. Significantly, the accuracy is greatly improved compared with the traditional load estimation method.

Counting the two evaluation indexes of load estimation results of each hypothetical customer under different methods, two box diagrams are drawn to observe the distribution of sample indexes of each method, which are shown in Figures 11, 12. According to the box diagrams, the sample median level of the two error evaluation indexes of the traditional load estimation method is the highest, and the error fluctuation range is the widest; the results show that the segmented simulation method achieves the suboptimal effect in the two evaluation indexes, and the error fluctuation is also controlled in a small range. It is worth noting that the median of the evaluation index samples of the proposed method is the lowest, the falling range of the error index samples is the narrowest, and the fluctuation range of the algorithm performance is smaller, showing the best stability.

FIGURE 11

FIGURE 12

Conclusion

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