Load prediction system and method based on load prediction pipeline framework language

Abstract

The invention relates to the technical field of power systems, and discloses a load prediction system and method based on a load prediction pipeline framework language, and the method comprises the steps: obtaining the load prediction configuration information of a user in a visual template mode through an LF_LFL generator, and generating a load prediction task table meeting the power load prediction demands of the user; converting the load prediction task table into a directed acyclic graph for executing the load prediction task, and automatically executing big data acquisition, cleaning, storage, exploration analysis, modeling features, model training, model evaluation and model application load prediction tasks of power load prediction according to the directed acyclic graph and according to a machine learning model library and a scheduling strategy meta-database in a big data technology support platform environment configured by a user through an LF_LFL interpreter, so that intelligent load prediction is achieved, the implementation difficulty of load prediction is reduced, and the load prediction efficiency is improved.

Description

technical field [0001] The invention relates to the technical field of power systems, in particular to a load forecasting system and method based on a load forecasting pipeline framework language. Background technique [0002] In order to meet the business needs of power grid planning, power dispatching, and power trading, power companies need to periodically forecast power loads from multiple dimensions such as regional, industry, and user dimensions. [0003] At present, power big data load forecasting involves the collection, cleaning, storage, exploration and analysis of heterogeneous big data, modeling features, model training, model evaluation, and model application processes. The big data platform and technology used in it are not only complicated to implement, but also It is constantly developing and changing, which has caused huge obstacles to the periodic load forecasting business of power companies. Contents of the invention [0004] The purpose of the present ...

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Problems solved by technology

[0003] At present, power big data load forecasting involves the collection, cleaning, storage, exploration and analysis of heterogeneous big data, modeling features, model training, model evaluation, and model application processes. The big data platform and technology used in it are not only complicated to implement, but also The continuous development and changes have caused huge obstacles to the periodic load forecasting business of power companies

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