Power Marketing Metering, Demand Analysis and Demand

Forecasting Based on Deep Learning

Xiaowan Zeng*, Liangbin Dong, Xiaoxi Fu, Jingyi Xie, Xinyan Wang and Jianing Liu

State Grid Fujian Marketing Service Center (Metering Center and Integrated Capital Center), Fujian, 350000, China

Keywords: Deep Learning; Power Marketing; Electricity Marketing Metering; Demand Analysis Analysis; Demand

Forecasting, Power Marketing

Abstract: At present, the power demand fluctuates greatly, and the demand analysis and processing is relatively

complex, and the real-time forecast demand is high, and these problems need to be solved. The purpose of

this paper is to study the analysis and demand forecasting of power marketing metering demand analysis and

demand forecasting based on deep learning, so as to solve the problems of inaccurate power demand

forecasting and low operational efficiency. In this paper, the initial research is carried out through the design

of system power marketing and related steps. Subsequently, the system adopts the hybrid structure of CNN

and LSTM, two deep learning algorithms, combined with microservice architecture technology, to achieve

efficient integration and deployment of the system. After the completion of the system, in order to verify the

effectiveness, stability and prediction accuracy of the system, this paper also applies the system in practice.

The results show that the system has multiple advantages, such as high accuracy, real-time, and effective

decision support. The research in this paper will provide a guarantee for the power price strategy formulation

and power dispatching optimization of power companies, ensure the high utilization efficiency of power

resources, and maintain the stable operation of the power grid. At the same time, the research in this paper

will also lay a good foundation for the further development and construction of smart grids in the future.

1 INTRODUCTION

Electricity demand forecasting has always been an

important part of power system management (Ahmed

and Basumallik, et al. 2024). In the face of growing

demand for electricity and increasingly complex

consumption patterns, it is clear that traditional

forecasting methods are no longer able to adapt to the

needs of the electricity market. At the same time,

demand analysis and analysis in power marketing

measurement is also an important part of power

system management, which deserves attention

(Bhatnagar and Yadav, et al. 2024). For example,

some scholars have proposed a time series analysis

method (Huang and Wu, et al. 2024), using ARIMA

power marketing to conduct systematic statistics and

analysis of historical demand analysis, and

establishing mathematical power marketing to predict

future power demand (Kumari and Yadagani, et al.

2024). However, this power marketing is not effective

in dealing with nonlinear and high-dimensional

demand analysis, and cannot effectively capture the

complex changes in power demand (Li and Cui, et al.

2024). At the same time, some scholars have

proposed to apply Support Vector Machine (SVM) to

the study of this problem. However, although SVM

can perform well on small-scale demand sets, it has

obvious shortcomings in large-scale and high-

dimensional demand sets, such as long market

research time and difficulty in coping with higher

computational difficulty, which makes the

application effect of this method very limited (Qian,

and Yang, et al. 2024). In 2020, some scholars

proposed that artificial neural methods can be used to

solve the problems of demand analysis and demand

forecasting of power marketing metering, but

although artificial neural methods can simulate

human brain neural networks through multi-layer

perceptrons, they are often sensitive to parameter

selection and the prediction effect is not stable

enough (Ran and Tay, et al. 2024). It can be seen that

although the above methods have their own

advantages, they cannot effectively deal with the

changing and complex power consumption patterns,

and have great limitations (Sapkota and Neupane, et

al. 2024), which cannot meet the current requirements

548

Zeng, X., Dong, L., Fu, X., Xie, J., Wang, X. and Liu, J.

Power Marketing Metering, Demand Analysis and Demand Forecasting Based on Deep Learning.

DOI: 10.5220/0013550800004664

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 3rd International Conference on Futuristic Technology (INCOFT 2025) - Volume 1, pages 548-554

ISBN: 978-989-758-763-4

for high-precision and real-time prediction of power

systems. Deep learning theory provides a new way of

thinking to solve these problems (Wang and Sun, et

al. 2024). Deep learning can automatically extract and

learn the features of the demand set by building multi-

layer neural networks, and has strong nonlinear

modeling capabilities and generalization performance

(Zhang, 2024). At the same time, deep learning is

particularly suitable for large-scale and multi-

dimensional power consumption demand analysis in

power demand forecasting. In this paper, we will

study a deep learning-based demand analysis and

demand forecasting power marketing to better

improve the accuracy and real-time performance of

demand forecasting, and improve the processing

speed of the system for demand analysis.

2 RELATED WORKS

2.1 Application of Deep Learning in

Electricity Marketing

Measurement

Deep learning is a machine learning technology that

can automatically learn the representations and

features of demand analysis, and it has a common

application in power marketing measurement. At

present, through the integration of CNN and LSTM,

sufficient useful information can be extracted from a

large number of power consumption demand

analysis. In addition, the analysis of temporal

requirements is automatically processed to discover

hidden patterns in them, thereby improving the

accuracy of the analysis.

2.2 Electricity demand forecasting,

electricity marketing

Electricity demand forecasting plays an important

role in the management of the power system and is a

key part. Deep learning power marketing, such as

CNN, LSTM, GRU, etc., can be applied to the power

system due to its important advantages in capturing

time-dependent and nonlinear relationships, and has

become an important tool in power marketing

metering, demand analysis, analysis and demand

forecasting. In general, LSTMs can be used for

periodic power demand forecasting such as daily and

monthly loads. At the same time, LSTM can also be

combined with CNN to build hybrid power marketing

and improve the effect of power demand forecasting.

2.3 Relevant Theoretical Basis

The first is the theory of time series analysis. The

processing and analysis of time series demand

analysis is also the focus of demand analysis and

demand forecasting of power marketing. Although

traditional methods such as autoregressive integral

moving average power marketing and exponential

smoothing are still effective, they have been

surpassed by deep learning methods. Second,

statistical learning theory. Statistical learning theory

is related to this study, which mainly includes SVM,

random forest, prediction adjustment technology, etc.

These theoretical courses play a certain role in the

selection and classification of characteristics of

power demand analysis. Finally, large demand

analysis and processing with distributed computing.

The demand analysis of power marketing metering is

very large, and how to carry out efficient storage,

processing and analysis is a key, which needs to rely

on technologies or platforms such as large demand

analysis and processing and distributed computing.

Currently, distributed computing platforms that can

be used include Hadoop and Spark.

3 RESEARCH METHODS

3.1 System Architecture Design

The architecture of the system adopts a hierarchical

architecture pattern, which includes multiple layers,

such as demand analysis layer and processing layer,

power marketing layer, display layer and application

layer. The requirements analysis layer is mainly

responsible for the collection and storage of

requirements analysis. The processing layer is mainly

responsible for pre-processing the requirements

analysis and performing feature engineering. The

power marketing layer is mainly responsible for

building and market research, and deeply learning

power marketing. The display layer is mainly

responsible for the visualization and display of results

of demand analysis. The application layer is mainly

responsible for the application and deployment of

power marketing; Secondly, module division. The

system is mainly divided into these modules. The

module is mainly responsible for collecting various

demand analysis sources, such as smart meters and

meteorological demand analysis, and user

information systems. The module needs to perform

various pre-processing of demand analysis, such as

demand analysis cleaning and predictive matching,

feature extraction, etc. The task of the module is to

Power Marketing Metering, Demand Analysis and Demand Forecasting Based on Deep Learning

549

select and design, market research, evaluate and

optimize electricity marketing. The results analysis

and presentation module analyzes the forecast results

and then displays them in the form of graphs and

reports. The module is responsible for the

management of the system, such as operation

monitoring, logging, and exception handling.

3.2 Demand Analysis, Collection and

Pre-Processing

In demand analysis and collection, the sources of

demand analysis mainly include smart meter demand

analysis and meteorological demand analysis, user

information, historical power load demand analysis,

etc. For example, the electricity consumption of each

unit of each user, such as hourly, daily, and monthly

electricity demand analysis. Meteorological demand

analysis. The main ones are temperature and

humidity, which are closely related to the demand for

electricity. User information includes the user's

electricity consumption category, geographical

location, etc. Historical power load demand analysis,

such as the historical load curve of the grid system,

can reflect the trend of power demand changes. The

demand analysis and collection technology includes

API interfaces, for example, the system can obtain

demand analysis from third-party platforms and

systems through API interfaces. Demand analysis

sensors such as smart meters or weather sensors can

be used to collect demand analysis in real time. In

addition, a requirements analysis warehouse (such as

Hadoop) can be used to store the collected

requirements analysis in a distributed warehouse

platform

In the requirements analysis pretreatment, first of

all, the requirements analysis cleaning needs to be

carried out. (1) Handling of market competition. The

treatment of market competition is mainly to fill in

the deleted or matched demand analysis, as detailed

in equation (1).·

{

}

iii

= mean(x) if x is missing x otherwise

(1)

In Eq. (1),

it is the first

point of demand

analysis in the demand set. If the requirement analysis

point matches, it needs to be filled

mean( )

missing

It refers to

the condition of whether the

requirements analysis points match or not.

(2) Detect outliers. In the requirements analysis

cleaning, outliers are also detected. In this article, the

method of Eq. (2) is used to identify and deal with

outliers.

−

(2)

In Eq. (2),

is the z-score value;

is a demand

analysis point;

is the mean, is the

standard

deviation.

Second, there is a need to change the electricity

market environment. Implement predictive matching

processing first. For example, if the electricity market

environment is scaled to a standard range, that is, 0-

1, and then the impact of the dimension is eliminated.

Normalization is initiated, converting the electricity

market environment to a standard normal distribution

with mean = 0 and standard deviation = 1. After that,

feature extraction begins. Extract and construct useful

features from the electricity market environment,

such as extracting date information based on dates.

Then, the electricity market environment is

consolidated. Merge demand analyses from different

sources to form a unified marketing set. Convert

demand analysis into a time series format or matrix

format for easy input for power marketing. Through

interpolation and synthesis, the demand analysis is

expanded, the amount of demand analysis is

increased, the demand analysis is enriched, and the

diversity of demand analysis is improved. Moving

averages are used to smooth out the requirements

analysis so that the impact of noise can be reduced.

3.3 Deep Learning Power Marketing

In this process, the power supply layer can input

various characteristic demand analysis. The customer

layer can extract the spatial characteristics of demand

analysis through N power supply stations. The power

server can be used to reduce the dimensionality of

features and reduce the amount of computation. The

LSTM layer is used to process the features extracted

by the client layer and to capture the presence of

pipette-dependent pipettes in the time series. The

marketing layer further processes the output of the

LSTM for demand forecasting. The output layer

outputs a predicted power demand. When conducting

market research and optimization, the forecast first

prepares a demand analysis, extracts features from the

original demand set, and completes the pre-

processing work. Its characteristics include time and

temperature and humidity. Subsequently, it is

necessary to carry out the preprocessing of demand

analysis, and the forecast matching processing of

INCOFT 2025 - International Conference on Futuristic Technology

550

demand analysis is carried out to ensure the scale of

the demand set, see Eq. (3).

x-min(x)

max(x) - min(x)

′

)

is the original demand analysis value, which

represents the original demand analysis point that has

not been processed for forecast matching.

min( )x

the minimum value of the demand concentration.

This value adjusts the starting point of the

requirements analysis so that the minimum value of

all requirements analysis points becomes 0;

max( )x

is the maximum value of the demand set, which is

used to adjust the end point of the demand analysis,

so that the maximum value of all demand analysis

points becomes 1;

is the demand analysis value

after the forecast is matched.

Then, the requirements analysis is divided. The

demand set is divided into market conditions, forecast

results, and marketing results, and the proportion is

70%, 15%, and 15%. Then, use the prepared demand

analysis market research to build the power marketing

and conduct market research. In market research,

MSE is used as a loss function to conduct market

research, and the MSE calculation is shown in Eq. (4).

i=1

MSE = (y - y )



(4)

In Eq. (4),

is the actual value;

is a predicted

value;

is the number of samples.

When researching power marketing in the market,

the parameters should also be adjusted through the

backpropagation algorithm to minimize the loss

function. The prediction involves the weight

calculation formula for gradient descent, as detailed

in Eq. (5).

ij ij

∂

←−

∂

)

In Eq. (5), is the weight

that connects the first

layer to

the layer;

It is the market integration

rate;

∂

the partial derivative of the loss

function for the weights.

For the evaluation of power marketing, the

performance evaluation of power marketing should

be carried out on the validation set, and the MSE of

the verification set should be calculated, see Eq. (4).

Finally, optimize electricity marketing.

Hyperparameters such as market convergence rate

and market size can be adjusted, and forecasting

adjustment techniques can be applied to optimize

power marketing, as shown in Eq. (6).

ropout Rate = p (e.g., p = 0.5)

)

In Eq. (6),

Dropout Rate

is the proportion of

neurons randomly selected and ignored in each

iteration of the market research. If this value is equal

to 0.5, 50% of the neurons will be temporarily ignored

with each iteration. After that, a grid search or a

random search is carried out to find the optimal

combination of hyperparameters and test the power

marketing in parallel. To do this, it is necessary to test

the performance of electric marketing on the

marketing results. Calculate the MSE of the

marketing results and thus obtain the test results.

4 RESULTS & DISCUSSION

The deep learning-based power marketing metering

demand analysis and demand forecasting system can

achieve excellent performance in practical

applications, and its advantages include:

4.1 Introduction to Electricity

Marketing Metering

Taking the 35KV transmission network as the

research object, the actual purchase demand was

tested through online marketing analysis. Among

them, the test time is from the beginning of 2023 ~

the end of 2023, with 300 surveyed users and a survey

range of 10km, as shown in Figure 1.

Figure 1: Actual results of electricity marketing and

demand

Power Marketing Metering, Demand Analysis and Demand Forecasting Based on Deep Learning

551

As can be seen from Figure 1, the demand for

electricity is smaller than that of electricity

marketing, so it is necessary to accurately predict to

improve the effect of electricity marketing. In the

application of the system in this paper, this paper

obtains the analysis of the operating requirements and

performance parameters of the system through

practical application, and evaluates all aspects of it.

See Table 1 for details.

Table 1: Comparison of predicted and actual power

consumption

Date Tim

Actual

Power

Consumpti

on (kWh)

Predicted

Power

Consumpti

on (kWh)

Error

(kW

Err

Rat

(%)

202

12-

00:0

1020 1015 5 0.4

202

12-

01:0

980 985 -5 0.5

202

12-

02:0

950 945 5 0.5

... ... ... ... ... ...

202

12-

23:0

1100 1095 5 0.4

The MSE of the system on the tester is 0.050 and

the MAE is 0.17. There is only a small error between

the forecast and the actual electricity demand, and the

error rate is generally within 0.5%. This shows that

the predictive ability and accuracy of this power

marketing are very good. Description: This table

shows a comparison between actual and forecasted

power consumption per hour for December 2023.

Error and error rate indicate the accuracy of the

forecast for this power marketing

4.2 Metering Demand Forecasting

In order to ensure that the power marketing metering

demand analysis and demand forecasting can achieve

good performance, and achieve high efficiency and

high effect results in the power marketing metering

demand analysis and demand forecasting tasks. This

paper adopts the design of power marketing based on

deep learning. The modeling prediction combines

CNN and LSTM algorithms in deep learning. The

power marketing design should first include the

power marketing structure, such as the power supply

layer and the customer layer, the power server, the

LSTM layer, the marketing layer and the output layer,

and the specific demand forecast results are shown in

Table 2.

Table 2: System performance metrics

Metric Training

Set

Validation

Set

Test

Set

Mean Squared

Error (MSE)

0.045 0.048 0.050

Mean Absolute

Error (MAE)

0.15 0.16 0.17

R(Coefficient

Determination

)

0.95 0.94 0.93

The system has a certain degree of real-time and

flexibility. The system can process and predict power

demand in real time, and has an advantage in the

analysis and analysis of power marketing metering

demand, which can help power companies to carry

out timely power dispatching and distribution

adjustment, and improve the adaptability and

response speed of the power grid. Note: The table

provides the specific performance of the system on

different demand sets, and can show the prediction

accuracy and generalization ability of the power

marketing, as shown in Figure 2.

Figure 2: Forecasts of electricity demand and power supply

As can be seen from Figure 2, both the demand

for electricity and the supply of electricity have

changed, showing an increase in demand and supply.

4.3 Reliability of Demand Forecasts

System architecture design, including demand

analysis layer, application layer, processing layer,

power marketing layer, and monitoring layer.

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552

Integrate these layers into this system; The

microservice architecture can ensure that each

functional module can be deployed and maintained

independently. Save the market research power

marketing as a separate file to facilitate the

deployment of applications later; Servitization of

modules. To this end, FLASH should be used to

encapsulate power marketing into a Web service and

provide a predictive interface for it. The

simplification of marketing demand analysis is to

package services into container images to ensure a

high degree of consistency and portability of the

deployment environment. To do this, build and run a

Docker container and test how the service works in

the container; Finally, it needs to be deployed to a

cloud platform, such as AWS, to achieve high

availability and scalability of the system, and the

reliability analysis of the requirements is shown in

Table 3.

Table 3: System application effects

Application

Scenario

Description Improvement

Effect

Power Dispatch

Optimization

Optimizing

power

distribution

based on

prediction

results to reduce

eak loa

Peak load

reduced by 5%,

dispatch

efficiency

increased by

10%

Electricity

Pricing Strategy

Adjustment

Adjusting

pricing strategy

based on

demand

prediction to

achieve supply-

demand balance

Price fluctuation

reduced by

15%, customer

satisfaction

increased

Power Grid

Stability

Improvement

Real-time

demand

prediction to

preemptively

identify

potential grid

instability

factors

Grid failure rate

reduced by

20%, response

time shortened

by 30%

The results in Table 3 illustrate the scenarios and

effects of the system application, showing that the

utility's operations have improved. Provide reliable

decision support. The application of the system can

provide accurate prediction results and efficient

demand analysis, and the application of the system

will provide powerful decision-making support for

the power company's electricity price strategy

formulation and scheduling optimization. In addition,

it can improve the efficiency of power resource

utilization, as shown in Figure 3.

Figure 3: The relationship between power marketing

capability and demand

As can be seen from Figure 3, the effect of power

marketing has improved, and it has gradually met the

power supply needs of users, which is more targeted.

Through the application of the system and its deep

learning hybrid power marketing, the system can

maintain strong robustness and high stability in

complex power consumption demand analysis, and

can adapt to various demand analysis fluctuations and

anomalies.

5 CONCLUSIONS

In the system proposed in this paper, it can be found

that the requirements analysis of the system is very

good after practical application. The demand analysis

results show that the value of the system in the

application of power demand forecasting is 995

MWh, and the actual power demand is 1000MWh,

the mean square error between the two is 0.05, the

mean absolute error is 0.17, and the error rate is 0.5%.

As a result, the system can efficiently and accurately

predict power demand, and significantly improve the

operational efficiency and decision-making

efficiency of power companies. At the same time, the

system has strong robustness, stability and high

economy, which can handle a large number of power

marketing metering demand analysis, and maintain

efficient operation and accurate demand analysis and

analysis capabilities. At the same time, the system can

also enhance the operational stability of the power

grid and reduce power waste for power companies.

The research results of this paper have certain

limitations, and there are controversies in demand

analysis and index selection in the analysis and

collection of power marketing metering demand and

power demand forecasting, and the analysis will be

Power Marketing Metering, Demand Analysis and Demand Forecasting Based on Deep Learning

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focused on in the future to provide support for the

construction of smart grid.

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