多模型融合技术在调度令运行时长实时预测中的应用研究

ShouTian Zhang, Zhengning Pang, WeiLong Yan, JingXian Qi, Jian Yang, FuQuan Zhao
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引用次数: 0

摘要

电网调度是一项极具挑战性的工作,需要调度人员在多个方面具有前瞻性,其中调度命令的操作是其核心任务之一。目前,调度命令持续时间的预测主要依靠经验丰富的调度员人工维护,不仅劳动强度大,而且对专业技术水平要求高。因此,现有系统在这方面的有效性难以保证。然而,由于业务场景的复杂性,目前还没有部署基于人工智能算法的系统。针对这一问题,本文采用多模型融合技术实现了调度命令运行持续时间的实时预测,并在浙江省电力公司进行了部署。本文的主要贡献在于提出了一种解决差异化特征融合后回归预测问题的策略。此外,本文还详细介绍了相关算法在浙江省电力公司的部署情况,并展示了实际应用结果,证明了所提解决方案的有效性和实用性。
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Application Research of Multi-Model Fusion Technology in Real-Time Prediction of Dispatch Order Operation Duration
Power grid dispatching is a challenging task that requires dispatchers to be forward-looking in multiple aspects, with the operation of dispatch orders being one of its core tasks. Currently, the prediction of dispatch order duration relies mainly on the manual maintenance of experienced dispatchers, which is not only labor-intensive but also demands a high level of expertise. As a result, the effectiveness of existing systems in this regard is difficult to guarantee. However, due to the complexity of business scenarios, there has been no deployment of systems based on artificial intelligence algorithms. To address this issue, this paper employs multi-model fusion technology to achieve real-time prediction of dispatch order operation duration and has deployed it in Zhejiang Electric Power Company. The main contribution of this paper lies in proposing a strategy to solve the problem of regression prediction after differentiating feature fusion. Additionally, the paper provides a detailed description of how the relevant algorithms were deployed in Zhejiang Electric Power Company and presents the results of practical applications, demonstrating the effectiveness and practicality of the proposed solution.
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