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

Abstract

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.

Keywords

• value chain
• internal simulation market system
• local outlier factor (LOF)
• abnormal data processing
• data fusion
• data dimensionality reduction