2.1

data preprocessing

The collected raw data was cleaned to remove invalid data, fill in missing values and correct errors. By using data quality analysis tools and technologies, in order to protect the privacy and security of data, it is necessary to desensitize and encrypt sensitive information, by using differential privacy, homomorphic encryption and secure multi-party computing technologies. To improve the readability and usability of the data, standardization and formatting are also needed, such as converting the data into uniform units and formats[2]. In order to better utilize the subsequent analysis and application, it is necessary to feature the data, that is, extract the features of the data and select and transform it to make it more suitable for the subsequent analysis and modeling requirements.

Figure 1:

Flow chart of data preprocessing

2.2

Data fusion and collaborative analysis

Data fusion and collaborative analysis are key steps in integrating data from different sources, aiming to reveal the inherent connections and value between data[3]. This requires the adoption of advanced data integration technologies, such as data warehouses and data lakes, for unified storage and management of multi-source data; By utilizing techniques such as data association, network analysis, and machine learning, data can be correlated, integrated, and collaboratively analyzed, including clustering analysis, association rule mining, and time series analysis, to discover potential patterns and trends. Through these methods, comprehensive monitoring, prediction, and optimization of the power system can be achieved, improving the safety and efficiency of the power system[4].

2.3

Safety and protection technology

Advanced encryption algorithms are used to encrypt sensitive data, such as symmetric encryption, asymmetric encryption and mixed encryption, to protect the security of data during transmission and storage. Implement access control and identity authentication technologies to ensure that only authorized users can access and use data and prevent unauthorized access and data leakage. Using firewall, intrusion detection system and security audit technology, to build a comprehensive security protection system, real-time monitoring and defense of potential security threats and attacks. Strengthen the security monitoring and situation awareness of the power system, and timely detect and handle the security incidents and abnormal behaviors through the real-time collection and analysis of the security information of the system[5]. Establish a sound safety management system and emergency plan, strengthen safety training and education, and improve the safety awareness and response ability of employees.

Figure 2:

Compact state and multi-source collaborative security application diagram of electric power big data

3.1

Power demand side management

Power demand side management (DSM) is a crucial strategy for enhancing energy efficiency, optimizing power structure, and ensuring supply security. It employs technical means, market mechanisms, and policy guidance to achieve efficient, reasonable, and controllable power consumption from the demand side. Key aspects include reducing basic demand through energy efficiency measures, realizing flexible load adjustment via demand response mechanisms, and improving power system elasticity and adaptability by promoting smart grid technology and distributed energy resources[6]. Implementing DSM not only helps reduce peak load and dependence on new generation capacity but also facilitates effective consumption of renewable energy, ultimately promoting efficient operation and sustainable development of the power market.

4.1

Advantages and disadvantages of data preprocessing methods

Data preprocessing is the key step in the data analysis process, which involves the operations such as cleaning, conversion, normalization and selection, and the purpose is to improve the quality of the data and make the subsequent analysis and modeling work more reliable and effective[7].

Table 1:

Advantages and disadvantages of data preprocessing methods

Data preprocessing significantly improves data quality through cleaning, transformation, normalization, and feature selection, providing a reliable foundation for subsequent analysis and modeling. Despite its advantages in enhancing data availability and simplifying model implementation, preprocessing also poses challenges such as potential overfitting, loss of useful information when removing noise and outliers, increased execution costs due to human involvement, and computational burden[8]. Therefore, while data preprocessing is crucial for effective data analysis, its pros and cons must be carefully weighed, and the preprocessing process should be designed to improve data quality while retaining key information, ultimately supporting accurate and efficient data analysis.

4.2

Advantages and disadvantages of data fusion and collaborative analysis methods

Data fusion and collaborative analysis methods can synthesize data from multiple sources, so as to improve the accuracy and integrity of data and capture the correlation between data, which plays an important role in the analysis and solution of complex problems.

Table 2:

Advantages and disadvantages of data fusion and collaborative analysis methods

Data fusion and collaborative analysis methods significantly improve data accuracy and completeness by integrating information from multiple sources, enabling analysts to capture correlations between different data, provide profound insights, and enhance solution quality for complex problems. These methods also improve the predictive and interpretive capabilities of models, supporting decision-making[9]. However, they face challenges such as data processing complexity, high computational costs, increased storage requirements, and reliance on expert knowledge and experience. While these methods offer substantial benefits, the associated technical and implementation challenges, including complexity, computational costs, storage demands, and the need for expert input, must be carefully considered to ensure the efficiency and accuracy of the data fusion and analysis process.

4.3

Advantages and disadvantages of safety protection technology

With the increasing awareness of data security and privacy protection, the application of security protection technology in data processing and analysis is becoming more and more important. These technologies can protect the privacy and integrity of data, detect and defend against multiple attack types, and ensure the security of data.

Table 3:

Advantages and disadvantages of safety protection technology

Security protection technologies are crucial in the data-driven era, ensuring data privacy and integrity while detecting and defending against various attacks. They provide essential security guarantees for data processing and analysis, maintaining trust and compliance with regulations, especially when dealing with sensitive information. However, these technologies can negatively impact system performance, consuming significant computing resources during encryption and decryption processes[10]. Regular updates and maintenance are necessary to maintain the effectiveness of protection measures, which increases management complexity and may lead to economic burdens. False positives and false negatives in security systems also need to be addressed through continuous technological improvements and adjustments. Despite these drawbacks, the importance of security protection technologies continues to grow as awareness of data security and privacy protection increases.