Quantitative data processing of whole service value chain for power grid internal simulation market

• Published in: Systems science & control engineering Vol. 12; no. 1
• Main Author: Liu, Nan
• Format: Journal Article
• Language: English
• Published: Taylor & Francis Group 31.12.2024
• Subjects: abnormal data processing; data dimensionality reduction; data fusion; internal simulation market system; local outlier factor (LOF); value chain
• ISSN: 2164-2583; 2164-2583
• DOI: 10.1080/21642583.2024.2342815

The internal simulation market system can stimulate employee initiative, reduce costs and improve information processing efficiency. However, the complexity of the internal simulation market poses a challenge to computing resources. Efficient data processing techniques are crucial for internal simulation market systems. In this paper, the internal simulation market model and the value chain theory are first put forward. Second, the internal simulation market construction within power grid enterprises is proposed. Then we propose a data mining-based collaborative fusion and processing method for multi-value chain quantitative data in power grid internal simulation markets to conduct data processing, including data fusion, abnormal data elimination and data dimensionality reduction, thereby improving the accuracy of information processing and data sharing. Finally, we validate the superior performance of the proposed method through simulations.