Scholarlink

With the continuous growth of global energy demand, energy information systems are facing problems such as low data processing efficiency and insufficient prediction accuracy, and urgently need to optimize solutions. This article aims to construct an efficient energy information system based on optimized grey model algorithm, in order to improve the management and utilization efficiency of energy resources. Firstly, the article analyzes the characteristics of current energy data, constructs a grey model, and then introduces an improved particle swarm optimization (PSO) algorithm to optimize the model parameters to improve prediction accuracy. The specific steps include data preprocessing, model establishment, parameter optimization, and result validation. In the experiment, historical energy consumption data is used for model training and testing. The results showed that the optimized model achieved a prediction accuracy of 98%. The final energy information system effectively integrated multiple data sources and could monitor and predict energy demand in real time, providing support for decision-making and significantly improving the scientificity and effectiveness of energy management, demonstrating good application prospects.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Construction of Energy Informatization System Based on Optimization Grey Model Algorithm

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Xiaotian Fu,
  • Qian Zhang,
  • Chuhang Xu,
  • Fen Luo,
  • Xiaofei Wang,
  • Xi Yang

摘要

With the continuous growth of global energy demand, energy information systems are facing problems such as low data processing efficiency and insufficient prediction accuracy, and urgently need to optimize solutions. This article aims to construct an efficient energy information system based on optimized grey model algorithm, in order to improve the management and utilization efficiency of energy resources. Firstly, the article analyzes the characteristics of current energy data, constructs a grey model, and then introduces an improved particle swarm optimization (PSO) algorithm to optimize the model parameters to improve prediction accuracy. The specific steps include data preprocessing, model establishment, parameter optimization, and result validation. In the experiment, historical energy consumption data is used for model training and testing. The results showed that the optimized model achieved a prediction accuracy of 98%. The final energy information system effectively integrated multiple data sources and could monitor and predict energy demand in real time, providing support for decision-making and significantly improving the scientificity and effectiveness of energy management, demonstrating good application prospects.